Thesis:

Are Military-Trained Family Physicians Better Prepared for Practice than Their Civilian Peers?

MAJ John Heflin, MC, USA

Faculty Development Fellowship

Madigan Army Medical Center

(edited for web page 21 July 1999)

Table of Contents

Page

List of Tables ii

Glossary iii

Introduction 1

Statement of Problem 4

Purpose of Study 5

Methods 6

Selection of Residency Programs 6

Selection of Study Subjects 6

Collection of Data 7

Analysis of Data 8

Results 10

Analysis of Non-respondents 10

Demographics 10

Descriptive Statistics 11

Military vs. Civilian Comparisons 12

Regression Analysis 13

Discussion 31

Conclusion 36

Citations 37

Bibliography 39

Appendix A: Questionnaire 42

List of Tables

Number Page

1. Study Programs and Controls 9

2. Demographics of Military and Matched Civilian Residency Graduates 20

3. Subject Area Likert Scores and Inclusion Into Clinical Practice 21

4. Military vs. Civilian Subject Area Likert Scores and Scope of Practice 22

5. Practice Characteristics of Military and Civilian Residency Graduates 23

6. Significant Preparedness for Practice Multiple Linear Regressions 24

7. Scope of Practice Multiple Logistic Regressions (Subject Areas Not 25

Practiced By At Least 25% of The Physicians)

8. Scope of Practice Multiple Logistic Regressions (Subject Areas Not 27 Practiced By 10-25% of The Physicians)

9. Significant Practice Organization Multiple Logistic Regressions 29

10. Significant Practice Location Multiple Logistic Regressions 30

Glossary

Metropolitan Area – An Office of Management and Budget defined area which meets the criteria established for a metropolitan statistical area, consolidated metropolitan statistical area, or primary metropolitan statistical area. Counties are used to define these areas except in New England where cities and towns are used.

Metropolitan Statistical Area (MSA) – An Office of Management and Budget defined area which contains at least one city with ³ 50,000 inhabitants or a Census Bureau defined urbanized area of at least 50,000 inhabitants and a total population of at least 100,000 (75,000 in New England). Counties are used to define the metropolitan statistical areas except in New England where cities and towns are used. Outlying counties may be included in the MSA if they meet specified requirements for commuting and other metropolitan characteristics.

Consolidated Metropolitan Statistical Area (CMSA) – An Office of Management and Budget defined area which meets the requirements of a MSA, has separate component urban areas, and a population of one million or more resides within the consolidated area. Counties are used to define the consolidated metropolitan statistical areas except in New England where cities and towns are used.

Primary Metropolitan Statistical Area (PMSA) – Office of Management and Budget defined areas which are the urban component areas within the consolidated metropolitan statistical areas. Counties are used to define the primary metropolitan statistical areas except in New England where cities and towns are used.

Rural Area - A county which does not fulfill the Office of Management and Budget criteria for a metropolitan area. It does not contain a urbanized area of ³ 50,000 people or a population which commutes to an adjacent metropolitan area.

Introduction

Family Practice residencies opened in 1969 in response to congressional efforts to create more primary care physicians. Medical school graduates seeking to become family physicians undertake three years of training in a military or civilian Family Practice residency program. These residencies are accredited by the Accrediting Council of Graduate Medical Education (ACGME) which establishes standards such as the ratio of supervising staff to residents, the skills to be taught, and a curriculum with required instruction in approximately forty subject areas. The ACGME standards and core curriculum help to standardize residency training but the residency programs have to adapt their teaching and training opportunities to meet the needs of their residents and the community where the residency is located. A principle difference between the military and civilian residency programs is that the military graduates incur an active duty service obligation of several years and must also be trained to serve as officers in the armed forces.

In this period of active duty service, the military family physicians have limited choice in the location of their practice, the organization of the practice, or the scope of practice to be provided. The military predominantly organizes its physicians into group practices with a solo practice available for only 6.8% and a partnership of two physicians available for only 5.9%.¹ By comparison, the civilians have 20.6% in solo practice and 10.7% in partnerships.¹ The military family physicians have different administrative practices and usually obtain non-clinical roles to include medical committees, military community committees, and officer duties such as commands and war time roles.² They have little interaction with third party payment systems and their malpractice coverage is provided by the government with the military physician having no involvement in the malpractice arrangements. Their clinical practices are also likely to be different. The Blount et al. study of practice content, circa 1985, identified several differences in the ages of the patients and the medical conditions encountered in Army and nonfederal Family Practices.³ Since this study, the military has implemented a new healthcare plan, TRICARE, which disenrolled most of the retirees age 65 and over. These practice content and organization differences represent military unique training requirements which should be accommodated in their residency curriculum in order to produce effective graduates.

The receipt of timely feedback from recent graduates on how well they were prepared for practice and their scope of practice is needed by the military and civilian residency programs in order to refine their training. Several surveys of Family Practice residency graduates have been conducted to evaluate their satisfaction with training, perceived preparation for clinical practice, scope of practice, and other practice patterns. These studies began in the early 1980’s and most are a decade old. The Minnesota, Virginia, Washington, Wisconsin, and Army surveys evaluated preparedness for clinical practice in many of the forty subject areas of ACGME mandated instruction and found that greater than 25% of the graduates were under-prepared in counseling, psychiatric disorders, family life cycle, hematology/oncology, nephrology, allergy and immunology, orthopedics (fracture care), community medicine, personal finance, office management, personnel management, business planning and professional liability.^4-8 In addition, greater than 25% of the Army physicians felt under-prepared in sports medicine, geriatrics and research.⁸ A more recent national survey in 1991 by Cantor et al. found that 77% of family physicians felt that their residency training did a good or excellent job preparing them for practice but preparation in community medicine and the business aspects of practice was poor with only 44% and 12%, respectively, feeling well prepared.⁹

The Bredfeldt et al. national survey of 1987 residency graduates has provided an indication that graduates from military residencies may be better prepared for clinical practice than graduates from civilian residencies. The preparation for practice score of military residency graduates was significantly better than those from university based programs, university administered community programs, or university affiliated community programs.¹⁰ The military graduates’ satisfaction with training score was also significantly better than those from university based programs and university administered community residencies. ¹⁰ This study was limited to global assessments and did not evaluate preparation in the individual subject areas of ACGME required instruction.

Additional factors, such as the presence of other residencies and gender, have been shown to affect the preparedness for practice and/or scope of practice. The Bredfeldt study also found significantly lower scores for satisfaction with training when other non-Family Practice residents were present at the clinical rotations compared to when no other residents were present.¹⁰ The Ellsbury et al. study of University of Washington network graduates found that the preparedness for practice scores were significantly lower for women in otolaryngology, urology, substance abuse, neurology, and prescribing drugs for the elderly.¹¹ The women were also significantly more likely to practice in urban settings, take salaried positions, work in non-private practice, work fewer hours per week in patient care, and perform fewer complex procedures.¹¹ It is known that women are underrepresented in military Family Practice residencies, 23.5% of the military graduates vs. 37% of the national graduates in 1995 are female, but the effect of gender in military residency graduates has not been evaluated.¹

The role of the type of hospital at the residency, community hospital vs. medical center, does not appear to affect the preparedness for practice but does impact the practice location of the graduates. In the Gwyther et al. study of North Carolina residency graduates, the medical center and community hospital graduates had similar areas of under-preparation but more of the community hospital graduates, 86% vs. 60%, established their practice in communities of less than 100,000 people.¹² The Davidson et al. study of California residency graduates, the medical center and community hospital graduates had similar scores on the American Board of Family Practice intraining exams and the board certification exam and more of the community hospital graduates, 67% vs. 56%, established their practice in communities of less than 100,000 people.¹³ These studies suggest that community hospital based residencies produce more graduates who practice in smaller communities.

The community size where a practice is located has been shown to affect the scope of practice of family physicians. Obstetrics care is an excellent example with a 1992 study of Kansas residency graduates in private practice finding that 72% of the physicians in communities smaller than 100,000 provide labor and delivery care compared to only 31% in communities larger than 100,000 people.¹⁴

Although the number of people residing within a community has traditionally been used to describe a community as rural or urban, it can be misleading and the United States Office of Management and Budget defined metropolitan areas represent a better classification system. The non-metropolitan areas under the metropolitan area classifications are rural and do not have an urbanized area with over 50,000 people or a population which commutes to an adjacent metropolitan area. The metropolitan areas all contain an urbanized area with 50,000 or more people and are classified as a metropolitan statistical area (MSA), consolidated metropolitan statistical area (CMSA), or primary metropolitan statistical area (PMSA). The MSAs have the greatest variability in size and range from just over 50,000 people in Enid, OK to 2.5 million in St. Louis, MO. The CMSAs and PMSAs are the most urban areas and occur where several urbanized areas combine into a consolidated area with separate identifiable urban areas and a population of at least one million people within the consolidated area. The PMSAs are the distinct urban areas within the consolidated region. A good example of a consolidated metropolitan area (CMSA) is Houston-Galveston-Brazoria which has a 1990 population of 3,731,029. There are eight counties (Brazoria, Chambers, Fort Bend, Galveston, Harris, Liberty, Montgomery, and Waller) which comprise this CMSA and three component PMSAs (Brazoria, Galveston-Texas City, and Houston).

The Geyman et al. combined analysis of the Minnesota, Virginia, and Washington residency program graduates provides an excellent example of how the use of the number of people within a community can be misleading for characterizing communities as rural or urban. The physicians appeared to be predominantly rurally located with 57% in communities smaller than 25,000 people and only 26% in communities larger than 100,000 people, but the metropolitan area classifications found only 36% to be in rural areas with 64% in metropolitan (urban) areas.¹⁵ The proportion of physicians practicing within a metropolitan area, 64%, was equal to the proportion of the population residing within a metropolitan area.¹⁵ There are no studies which utilize metropolitan area classifications to evaluate the relationship between community size and the scope of practice or the relationship between the type of residency hospital and the community size where the graduates practice.

The geographic region (state) where the practice is located is associated with great variability in the practice organization. In a 1995 national survey of family physicians, the variation in practice organizations ranged from 5.8 to 58.5% in solo practice, 3.4 to 22.4% in partnership practice, 11.7 to 50.8% in single specialty group practice, and 4.8 to 46.7% in multi-specialty group practice based upon the geographic region (state).¹ For example, Minnesota has 5.8% in solo practice, 3.4% in partnership, 44.2% in single specialty group practice and 46.4% in multi-specialty group practice while the numbers in Wyoming are 58.5%, 4.9%, 30.5%, and 6.1% respectively.¹ This variability in practice organization was also present in the early 1980’s follow-up of Wisconsin, Virginia, Minnesota, and Washington residency graduates with 7.5-27.3% in solo practice, 10.5-38.1% in partnership practice, 26-49.4% in single specialty group practice, and 1.9-33.1% in multispecialty group practice.^4-7 These studies are of limited value for understanding the practice organizations entered by recent Family Practice residency graduates as the subjects completed their residency from one to twenty years prior.

Statement of Problem:

There is a paucity of published information in the past decade on the effectiveness of military or civilian family medicine residency programs in preparing their graduates for clinical practice and no published studies have examined military Family Practice residencies in the Army, Navy, and Air Force. The past findings of under-preparation in counseling, psychiatric disorders, family life cycle, hematology / oncology, nephrology, allergy and immunology, orthopedics, community medicine, personal finance, office management, personnel management, business planning, and professional liability are concerning as these are among the core contents of family medicine and it is not known whether these deficiencies have been rectified. In addition, no study has attempted to control for the resident, community, and residency program factors which affect the preparedness of practice and the scope of practice.

Purpose of Study:

This survey is designed to evaluate the preparedness for practice and the scope of practice established by recent graduates from matched military and civilian family medicine residency programs. It will focus on thirty-seven subject areas of required instruction and will also assess practice patterns to include: the practice organization, practice location, time distribution of work activities, and average number of patient visits per week. The primary objective is to compare military and civilian graduates on preparedness for practice in the required subject areas. The secondary objective is to compare military and civilian graduates on their scope of practice. Regression analysis will be utilized to identify and control for the resident, community, and residency program characteristics which affect the preparation for practice and the scope of practice.

Methods

This study utilized a mailed survey to compare graduates from matched military and civilian Family Practice residency programs. Institutional Review Board Approval was obtained from Madigan Army Medical Center and the University of Washington with additional reviews performed by the Navy Clinical Investigations and Air Force Human Use Committees.

Selection of Residency Programs:

Each of the fourteen military family practice residency programs in operation from 1995-1997 was matched to three civilian family practice residencies to control for the effects of the geographic region, size of the residency, experience of the residency, presence of other residencies, hospital type, and community size on the graduate’s preparation for practice and scope of practice. The American Academy of Family Physicians Directory of Family Practice Residency Programs, years 1995-7, was the sole source of information used in the matching process.^16-18 The matching protocol used the following criteria: 1) located in the same or adjacent state, 2) number of residents graduating per year is within ± 25%, 3) similar program age as of 1995 (> 15 years, 10-15 years, 5-10 years, or < 5 years), 4) number of other residencies in hospitals used for training (0, 1, 2, or ³ 3), 5) hospital type - medical center vs. community hospital (medical centers are described as regional hospitals, referral centers or tertiary care while community hospitals have none of these descriptions), 6) residency setting (inner city, urban, suburban or rural) with one level of difference permitted. All of the military residencies and thirty-seven of the forty-two civilian programs provided the names of their graduates (Table 1).

Selection of Study Subjects:

A sample size calculation indicated that 132 subjects were required in each group for b = .20, a = .05, power (1-b ) of 80%, variance of 2.1, and a detectable difference of 0.5 on a 10 point Likert scale. Adjusting for an expected response rate of 70%, a sample size of 200 per group was chosen to preserve the power of the study. Stratified random sampling was used to select the subjects from the pool of 241 military and 620 civilian family physicians who graduated from the fourteen military and thirty-seven civilian Family Practice residency programs during 1996 and 1997. These graduation years were selected as the interval of 18-30 months from graduation to receipt of the survey would allow the subjects to assess the quality of their training and establish a clinical practice while minimizing recall bias.

Collection of Data:

A 78 question confidential survey (Appendix A) was developed and pretested for ease of completion and content validity. The survey was administered via three mailings in January, February, and March of 1999. The preparedness for practice and scope of practice were assessed in each of the thirty-seven areas of required instruction - Human Behavior and Mental Health (alcohol / substance abuse, counseling, psychiatric disorders, family life cycle), Adult Medicine (cardiology, endocrinology, pulmonary, hematology / oncology, gastroenterology, infectious diseases, rheumatology, nephrology, allergy and immunology, neurology, women’s health), Obstetrics, Gynecology, Surgery, Orthopedics, Urology, Otolaryngology, Sports Medicine, Emergency Medicine, Pediatrics (pediatrics, newborn nursery), Community Medicine (community medicine, health promotion), Geriatrics, Dermatology, Diagnostic Imaging and Nuclear Medicine, Research and Scholarly Activity, and Practice Management (personal finance, office management, personnel management, business planning, managed care, professional liability). Ten point Likert scales, with anchors of "very poorly prepared" at 1 and "very well prepared" at 10, were used to measure the preparedness for practice. "Yes" or "No" questions on whether the subject was included in the graduate’s medical practice were used to measure the scope of practice. In order to minimize confusion about what would constitute inclusion of a subject area into clinical practice, two examples were included in the instructions on the survey.

Four questions were included to measure the practice organization, practice location, time distribution of work activities, and average number of patient visits per week. The respondents were instructed to select their practice organization from eight choices - Solo practice, Partnership of two physicians, Single-specialty group of three or more physicians, Multi-specialty group, Teaching program, Emergency department, Inpatient care, and Other. The respondents were asked to provide the ZIP code where their practice is located. The practice location was coded according to the 1990 metropolitan area classification for the county where the ZIP code is located. The respondents were also asked to indicate the percentage of their work time spent in each of five activities – Patient care, Administrative work, CME, Teaching, and Other.

Additional information to include the gender, age, and board certification status of each study subject was obtained from publicly available sources to include the American Medical Association (AMA) Doctor Finder service on the Internet, the American Board of Medical Specialties (ABMS) Certified Doctor service on the Internet, the AMA’s published directory of physicians, and the ABMS’s published directory of board certified physicians.^19,20 The military status, location, type of hospital, university affiliation, age, number of residents per year, number of other residencies present, and number of hospital beds for each residency program was obtained from the 1995-1997 editions of the American Academy of Family Physicians Directory of Family Practice Residency Programs.^16-18 The residency location was also coded according to the 1990 metropolitan area classification for the county where the residency is located.

Analysis of Data:

The data was initially analyzed with simple descriptive statistics. Intergroup comparisons between the military and civilian program graduates were performed with Mann-Whitney and Chi Square analysis as appropriate for ordinal and categorical data. The preparation for practice Likert scores and time distribution of work activities were analyzed with the Mann-Whitney test. The respondent age, residency program age, number of residents per year, and number of beds in the hospital were also analyzed with the Mann-Whitney test as they did not have a Normal distribution. The board status, gender, scope of practice and other practice pattern characteristics were analyzed with the Chi Square test.

Multiple linear and logistic regressions were performed to determine which factors significantly affect the preparedness for practice, scope of practice, practice organization and practice location of the residency graduates. The gender (male vs. female), respondent age, graduation year, military status of the residency (military vs. civilian), residency location (rural, MSA, CMSA, or PMSA), type of hospital at the residency (community vs. medical center), university affiliation of the residency (no affiliation, university affiliated community program, university administered community program, or university based program), residency program age, number of residents per year, number of other residencies present (0, 1, 2, or ³ 3), and the number of hospital beds in the residency were included as independent variables to represent the resident, community, and residency program characteristics which affect the preparation for practice, scope of practice, and practice patterns. In the scope of practice multiple logistic regressions, the Likert score in the subject area and the practice location (rural, MSA, CMSA, PMSA, or outside the U.S.) were added to the independent variables. For the practice organization and practice location multiple logistic regressions, the average preparedness for practice score was added to the independent variables.

Spearman’s correlation identified a high degree of correlation between gender, military status of the residency, residency location, type of hospital at the residency, university affiliation of the residency, residency program age, number of residents per year, number of other residencies present, the number of hospital beds, practice location, and practice organization. As a result, stepwise regressions were precluded for these variables. Age and graduation year were added to the regressions in a stepwise fashion and the best linear and logistic regression models were obtained with all of the independent variables included. Normal probability plots of the residuals were performed to assess the linear regressions and the significance of dummy variables was confirmed with a Partial F test. For the logistic regressions, a Hosmer and Lemeshow Goodness of Fit Test was performed. In order to be considered for clinical significance, a regression must achieve statistical significance and should account for at least 10% of the variability in the outcome (adjusted R square or Cox and Snell R square ³ .10).

Table 1 - Study Programs and Controls

Military Program	Matched Civilian Programs
Camp Pendleton FPRP, CA	Sutter Medical Center of Santa Rosa FPRP, CA	Kaiser Permanente Medical Center – Fontana, CA
USAF - Travis Family Practice, CA	Glendale Adventist Family Practice, CA	University of California Davis Family Practice, CA
USAF - Eglin Family Practice, FL	Rome Family Practice, GA	Tallahassee Memorial Regional Med. Center, FL	Halifax Medical Center FPR, FL
Naval Hospital Dept. of Family Practice, FL	University of Alabama – Huntsville, AL	Florida Hospital Family Practice Residency, FL	Columbus Family Practice, GA
Pensacola Naval Hospital, FL	Bayfront Medical Center FPRP, FL	Jacksonville Family Practice, FL
USA – Fort Benning Family Practice, GA	Anderson Family Practice, SC	University of Tennessee Family Medicine, TN	University of Tennessee – St. Francis Hospital, TN
USA – Fort Gordon Family Practice, GA	Greenville Family Practice, SC	Medical College of Georgia, GA	Family Health Center – Family Practice, GA
Department of the Army – Tripler, HI	Kaiser Permanente Medical Center FPRP, CA	White Memorial Medical Center FPRP, CA	Valley Medical Center FPR, WA
USAF Medical Center Scott/SGHF, IL	Indianapolis St Francis FPR, IN	Resurrection Medical Center, IL
Malcolm Grow USAF, MD	Riverside Family Practice Center, VA	Family Practice Franklin Square, MD
Womack Army Medical Center, DOFM, NC	Spartanburg Family Medicine Residency, SC	East Carolina University Dept. of Family Med., NC	Richland Memorial Hospital FPR, SC
DeWitt Army Community Hospital, VA	Wheeling Hospital Family Health Center, WV	Clarksburg Family Practice, WV	Chesterfield Family Practice, VA
Puget Sound Family Medical Residency, WA	Idaho State University FPR, ID	St. Peter Hospital Family Practice Program, WA	Central Washington Family Medicine, WA
Madigan Army Medical Center, WA	UWMC Family Medicine Residency Program, WA	Group Health Cooperative FPR, WA	Tacoma Family Medicine, WA

Results

Analysis of Non-respondents:

There were 283 responses from the 400 subjects for a response rate of 71%. An analysis of the non-respondents found no statistically significant differences in their age, gender, graduation year, or proportion with board certification compared to the respondents. There were also no significant differences in the residency program age, number of residents per year, type of hospital, or metropolitan area classification of the residency location. The non-respondents were significantly more likely to graduate from civilian residencies (39.0% of civilian graduates are non-respondents vs. 19.5% of military graduates, p < .001).

Demographics:

The demographic characteristics of the military and civilian residency graduates are listed in Table 2. The mean age of the subjects is 33.6 years and the median age of 32 is the same in both groups. The majority of the graduates are male with a significantly larger proportion of men in the military graduates (80.1% vs. 62.3%, p = .001). The age of the residency program (median 24 years), median number of residents per year (10 vs. 8), and the number of other residency programs present are similar for the graduates of military and civilian residency programs. Army run residencies account for 39.5% of the military residency graduates with 29.5% from Navy programs and 31% from Air Force residencies.

There are several significant differences in the residency programs of these military and civilian residency graduates. A larger proportion of the military graduates (56.5% vs. 44.3%, p = .041) were trained in community hospitals. Although most of the military and civilian graduates (66.5% and 58.2%, respectively) are from residencies located within a MSA, significantly more of the military graduates are from programs located within CMSAs (23% vs. 4%, p < .001) and significantly less are from programs within a rural area (0% vs. 5.7%, p = .003) or PMSA (10.5% vs. 32%, p < .001). The military and civilian residency graduates are very different regarding the type of university affiliation present at their residency. The civilian graduates are predominantly from programs which have an association with a university and significantly more were trained in university affiliated community programs (72.1% vs. 14.9%, p < .001), university administered community programs (9% vs. 0%, p < .001), and university based programs (16.4% vs. 0%, p < .001). In contrast, most of the military graduates were trained in residencies which do not have an association with a university (85.1 vs. 2.5%, p < .001). The military graduates were also trained in smaller hospitals (median number of beds - 200 vs. 470, p < .001).

Despite the matching process, there are significant differences between the military and civilian residency graduates in gender composition, type of hospital at the residency, residency location, university affiliation of the residency, and number of beds in the hospital. These factors are also are expected to have some influences upon the preparedness for practice, scope of practice and other practice patterns established by residency graduates and are likely to confound simple comparisons between the military and civilian residency graduates. Regression analysis will be required to control for these differences and determine the true impact of the military status of the residency.

Descriptive Statistics:

The preparation for practice Likert scores for all respondents are listed in Table 3. The average score of 6.6 and the occurrence of median scores of 6 or higher in thirty of the thirty-seven subject areas indicates that the graduates felt well prepared for clinical practice. The lowest scores were in Research and Scholarly Activity and the practice management disciplines of Personal Finance, Office Management, Personnel Management, Business Planning, Managed Care, and Professional Liability where the median scores were all five or less. At least twenty-five percent of the respondents had a score of three or less in Office Management, Managed Care, and Professional Liability and half of the respondents had a score of three or less in Personal Finance, Personnel Management, and Business Planning. These low scores and the substantial proportion of respondents with low preparedness scores is indicative of poor preparation in the practice management disciplines.

The scope of practice questions indicate that most of the thirty-seven subject areas are included in the clinical practice of these family physicians (Table 3). There were eight subject areas which are not included in the clinical practice of over 25% of the residency graduates - Obstetrics, General Surgery, Newborn Nursery, Research and Scholarly Activity, Diagnostic Imaging and Nuclear Medicine, Personal Finance, Office management, and Business Planning. The lowest inclusion into clinical practice occurred with Research and Scholarly Activity which only 28.4% of the graduates perform. The disciplines of Alcohol and Substance Abuse, Counseling, Family Life Cycle, Nephrology, Emergency Medicine, Community Health, Geriatrics, Personnel Management, Managed Care, and Professional Liability are also notable with 10-25% of the respondents not including them in their practice but the significance of this finding is uncertain.

Military vs. Civilian Comparisons:

The preparation for practice Likert scores for the military and civilian residency graduates are listed in Table 4. An overall level of good preparation for clinical practice was present in both groups with average scores of 6.6 (median score) and median scores between 6 and 9 in at least thirty of the thirty-seven subject areas. The military and civilian residency graduates both felt poorly prepared for practice in the practice management disciplines with twenty-five percent or more of their respondents having a score of three or less in Personal Finance, Office Management, Personnel Management, Business Planning, Managed Care, and Professional Liability. Significant differences are present in the preparedness for practice scores of the military and civilian graduates in several subject areas. The military graduates have significantly higher scores in Obstetrics (9 vs. 8, p < .001), Gynecology (9 vs. 8, p = .015), General Surgery (both median scores 7 but military with higher mean, p = .029), Orthopedics (8 vs. 7, p < .001), Urology (both median scores 7 but military with higher mean, p < .001), Sports Medicine (8 vs. 7, p = .001), Research and Scholarly Activity (6 vs. 4, p < .001), and Diagnostic Imaging and Nuclear Medicine (both median scores 6 but military with higher mean, p = .003) while the civilians' scores are significantly higher in Community Medicine (8 vs. 7, p = .002), Geriatrics (8 vs. 7, p = .039), Managed Care (5 vs. 4, p = .005), and Professional Liability (5 vs. 4, p = .004).

There are several significant differences in the scope of practice for the graduates of military and civilian residencies (Table 4). The military graduates are significantly more likely to include Obstetrics (75.2 vs. 44.7%, p < .001), Emergency Medicine (88.5 vs. 75.9%, p =.007), and Diagnostic Imaging and Nuclear Medicine (73.4 vs. 58.7%, p = .013) while the civilian residency graduates are significantly more likely to include Alcohol and Substance Abuse (88.5 vs. 79.1%, p = .043), Rheumatology (97.3 vs. 91.1%, p = .040), Geriatrics (94.6 vs. 72.7%, p < .001), Personal Finance (80.2 vs. 53.9%, p < .001), Business Planning (60 vs. 46.1%, p = .028), Managed Care (93.5 vs. 78.4%, p .001), and Professional Liability (90.6 vs. 64.2%, p < .001) in their clinical practice. The difference in the provision of Rheumatology care is of no clinical significance as over 90% of the military and civilian trained physicians provide this care.

The practice characteristics of the military and civilian graduates are listed in Table 5. Board certification in Family Practice is very common and 96% of the graduates are board certified. The practice organizations are similar with over 60% belonging to a single specialty or multi-specialty group practice, but significantly fewer of the graduates from military residencies enter a solo practice (1.3 vs. 12.3%, p < .001) or a two person partnership (1.3 vs. 8.2%, p = .004). The majority of the practices are located within a metropolitan area with 56% of the military and 69% of the civilian graduates practicing in such an area. There were no significant differences between the military and civilian graduates in their distributions within MSAs, CMSAs, and PMSAs but a smaller proportion of the military graduates practice in rural locations (18.8 vs. 31.1%, p = .016) and a larger proportion practice outside the United States (25.6 vs. 0%, p < .001). It is notable that 18.8% of the military graduates and 31.1% of the civilian graduates practice in a rural location whereas none of the military graduates and only 5.7% of the civilian graduates were trained in rurally located residencies. It is also noted that the only physicians practicing outside of the US are military.

In their work activities, the military graduates do spend a significantly smaller proportion of their work time in patient care (75 vs. 85%, p <.001) and a larger proportion in administrative work (15 vs. 10%, p < .001) than the graduates from civilian residencies. A subgroup analysis did not find any significant differences in the distribution of work time for Army, Navy, and Air Force Family Practice residency graduates. The military graduates also have 10% less patient visits per week (90 vs. 100, p = .002) than their civilian peers. There appears to be a match between the 10% lower number of patient visits per week and the 10% lower proportion of work time in patient care activities for the military graduates.

These comparisons indicate that the military status of a residency may be responsible for significant differences in the preparation for practice, scope of practice, and practice characteristics of the graduates. Regression analysis is needed to control for the differences in gender composition, type of hospital at the residency, residency location, university affiliation of the residency, and number of beds in the residency hospital in order to determine the true effect of the military status of the residency.

Regression Analysis:

In the multiple linear and logistic regressions, the age of the graduate, age of the residency, number of residents per year, and the number of hospital beds at the residency have a range over which they may be increased or decreased. The valid ranges for these factors are 29 to 50 years for the graduate's age, 4 to 27 years for the age of the residency program, 3 to 15 for the number of residents per year, and 70 to 1470 for the number of hospital beds at the residency. The results of the multiple logistic regressions are reported in the tables with an increment of change of one year for the age of the graduate, one year for the age of the residency, one resident for the number of residents per year, and one bed for the number of hospital beds at the residency. A larger change is permissible as long as it is within the valid ranges. The new odds ratio would be calculated as eD b where D is the increment of change.

The multiple linear regressions for the preparedness for practice scores were clinically significant, F test < .05 and adjusted R square ³ .10, in only six of the thirty-seven subject areas - Obstetrics, Gynecology, Orthopedics, Office Management, Personnel Management, and Business Planning (Table 6). The regression analysis fail to confirm that the military status of the residency is responsible for differences in the preparedness for practice scores in eleven of the twelve subject areas identified in the military vs. civilian comparisons. The military status of the residency is significantly associated with the preparation score only in Obstetrics where the civilian residency graduates feel less prepared than the military graduates and score 1.41 lower. The seven resident, community, and residency program characteristics which are significantly associated with the preparation for clinical practice scores include gender, age, the military status of the residency, the number of other residencies present, the residency location, the residency program age, and the number of hospital beds in the residency. It is important to note that the largest adjusted R square is less than .25 so these factors explain less than 25% of the variability in the preparedness for practice scores.

Gender has a significant effect on the preparedness scores in Obstetrics, Gynecology, and Personnel Management. Women feel better prepared than men for practice in Obstetrics (score .55 higher) and Gynecology (score .64 higher) but less prepared in Personnel Management (score .86 lower). The presence of other residencies also has mixed effects on the preparation scores. Some interesting patterns occur in the practice management disciplines. The graduates feel less prepared for clinical practice as the age of the graduate or the age of the residency program increases, but they feel better prepared for clinical practice as the number of hospital beds in the residency increases. For each one year increase in the age of the residency program (maximum increase = 23 years), the preparation scores decrease by .09 in Office Management and Personnel Management and by .07 in Business Planning. For each one year increase in the age of the graduate (maximum increase = 21 years), the preparation scores decrease by .08 in Office Management and by .07 in Personnel Management. With each one bed increase in hospital size (maximum increase = 1400 beds), the preparedness scores increase by approximately .002 in Office Management, Personnel Management, and Business Planning. The residency location also has an interesting effect with graduates from rural residencies feeling better prepared for practice in Office Management than the graduates from programs located within a MSA, CMSA, or PMSA (scores 2.6, 1.8 and 1.7 lower, respectively). The rural residency graduates also feel better prepared in Personnel Management than the graduates from residencies located within a MSA (score 2.0 lower).

The multiple logistic regressions for the scope of practice were statistically significant and had an R square ³ .10 in twenty-three of the thirty-seven subject areas. This includes all of the subject areas which are not practiced by at least 25% of the physicians (Obstetrics, General Surgery, Newborn Nursery, Research and Scholarly Activity, Diagnostic Imaging and Nuclear Medicine, Personal Finance, Office Management, and Business Planning) and all but one of the subject areas which are not practiced by 10-25% of the physicians (Alcohol and Substance Abuse, Counseling, Family Life Cycle, Nephrology, Emergency Medicine, Community Medicine, Geriatrics, Personnel Management, Managed Care, Professional Liability) - Tables 7 and 8. The regression analyses confirm that the military status of the residency is responsible for significant differences in the scope of practice in Obstetrics, Diagnostic Imaging and Nuclear Medicine, and Professional Liability as had been identified in the military vs. civilian comparisons but fail to confirm a significant association with the scope of practice in Alcohol and Substance Abuse, Rheumatology, Emergency Medicine, Geriatrics, Personal Finance, Business Planning, or Managed Care. A significant association between the military status of the residency and the scope of practice in General Surgery was also identified. The graduates from civilian residencies are significantly less likely to include Obstetrics, General Surgery and Diagnostic Imaging (odds ratio .09, .14, .14, respectively) in their clinical practice but are more likely to include Professional Liability (odds ratio 6.32) in their practice than the graduates from military residencies.

The other ten resident, community, and residency program characteristics which are significantly associated with the scope of practice include the gender, age, graduation year, the residency location, the university affiliation of the residency, residency program age, the number of residents per year, the number of other residencies present, the practice location, and the preparedness for practice score in the subject area. In spite of the large number of factors which are significantly associated with the scope of practice, the largest R square is .276 and over 70% of the variability in the scope of practice is not due to these factors. The preparedness for practice score in the subject area is the most consistent determinant of the scope of practice. A higher preparedness score increases the likelihood of a subject area being included in the clinical practice - odds ratios from 1.18 to 2.21 for a one point increase in the preparedness score to a maximum of 4.5 to 1269 for a nine point increase in the preparedness score. The practice location has an interesting effect with the graduates who practice within a MSA or CMSA being less likely to include Alcohol and Substance Abuse, Family Life Cycle, and Nephrology in their clinical practice (odds ratios from .10 to .23) than those who practice in rural areas. The residency location also has an interesting effect with the graduates from residencies located within an MSA, CMSA or rural area being less likely to include Obstetrics, Newborn Nursery, Research and Scholarly Activity, or Family Life Cycle in their clinical practice (odds ratios from .08 to .18) compared to the graduates from residencies located within a PMSA. General Surgery is an exception to this pattern with the graduates from residencies within a CMSA being more likely to include it in their practice (odds ratio 9.5) than the graduates from residencies within a PMSA. The presence of other residency programs at the residency reduces the likelihood of a graduate including Newborn Nursery, Personal Finance, Office Management, Geriatrics or Community Medicine in his/her clinical practice (odds ratios from .25 to .38) compared to the graduates from programs where no other residencies are present.

The age of the graduate is associated with the scope of practice in the practice management disciplines. Older graduates are more likely to include Business Planning, Personnel Management, and Professional Liability in their clinical practice - odds ratios from 1.13 to 1.19 for a one year increase in age to their maximums of 11.67 to 36.27 with a 21 year increase in age. The residency program age is associated with the practice of Nephrology and Professional Liability with the graduates from older programs being more likely to include them in their clinical practice. The odds ratios are 1.11 and 1.15, respectively, for a one year increase in residency age and increase to their maximum of 11.99 and 24.46, respectively, with a 23 year increase in the residency age. The graduation year has mixed effects with the 1997 graduates being less likely to include Newborn Nursery (odds ratio .44), Family Life Cycle (odds ratio .24), or Nephrology (odds ratio .36) in their clinical practice but more likely to include Personal Finance (odds ratio 1.89) compared to the 1996 graduates. The university affiliation of the residency also has mixed effects on the scope of practice. The graduates from residencies with a university affiliation of some type are more likely to include General Surgery (odds ratios 7.3 to 26.5) or Diagnostic Imaging and Nuclear Medicine (odds ratio 5.8) in their clinical practice and less likely to include Counseling (odds ratio .05) compared to graduates from residencies without a university affiliation. Gender and the number of residents per year are associated with the scope of practice in Emergency Medicine and Personal Finance. Women are less likely to practice Emergency Medicine (odds ratio .43) or Personal Finance (odds ratio .42) compared to men. An increase in the number of residents per year in the residency program also reduces the likelihood of a graduate including Emergency Medicine or Personal Finance in his/her practice. The odds ratios are .73 and .85, respectively, for a one resident increase and decrease to their minimum of .02 and .13, respectively, with an increase of 12 residents per year.

The multiple linear and logistic regressions have shown that the military status of the residency accounts for few significant differences in the preparedness for practice or scope of practice in the thirty-seven subject areas of ACGME required instruction. The resident, community, and residency program characteristics which are significantly associated with the preparedness for practice or scope of practice include gender, age, graduation year, military status of the residency, residency location, university affiliation of the residency, residency program age, number of residents per year, number of other residencies present, the number of hospital beds at the residency, practice location, and the preparedness for practice score in the subject area. The only characteristic that is not significantly associated with the preparedness for practice or scope of practice of the residency graduate is the type of hospital (community hospital vs. medical center) at the residency. Even with the large number of significantly associated factors, less than 30% of the variability in the preparedness for practice or scope of practice is explained.

The multiple linear regressions evaluating the average number of patient visits per week, the percentage of time in patient care, and the percentage of time in administrative work were statistically significant and confirm that the military status of the residency is responsible for significant differences in the distribution of work time and the number of patient visits per week as had been identified in the military vs. civilian comparisons. The military status of the residency is the only factor that was significantly associated with the percentage of work time spent in patient care or administrative work. A graduate from a civilian residency will spend 14.3% more of his/her work time in patient care (t = .001) and 13.0% less time in administrative work (t = .000) compared to a graduate from a military residency. The graduates from civilian residencies will have 22.2 more patient visits per week than their military trained peers (t = .011). The other factor that is significantly associated with the average number of patient visits per week is gender. Women have 13.0 less patient visits per week than men (t = .007). In spite of these significant associations, the adjusted R square values are only .077 for the number of patient visits per week, .085 for the percentage of time in patient care, and .099 for the percentage of time in administrative work. Thus, over 90% of the variability in the percent of time in patient care, percent of time in administrative work and average number of patient visits is not due to military status or gender.

The multiple logistic regressions evaluating the practice organization were clinically significant for solo practice, partnership of two physicians, and teaching program (Table 9). These regressions confirm that the military status of the residency is responsible for differences in solo and partnership practices as had been identified in the military vs. civilian comparisons and also found that the military status of the residency is significantly associated with practicing in a teaching program. The graduates from civilian residencies are significantly more likely to practice in a solo practice or partnership (odds ratios 29.7 and 8027.4, respectively) and less likely to practice in teaching programs (odds ratio .02) than military residency graduates. The lower likelihood for military physicians to practice solo or in a two physician partnership is not surprising as these types of practice are seldom used in the military.

The other resident, community, and residency program characteristics which are significantly associated with the practice organization include the residency location, the university affiliation of the residency, and the age of the graduate. The residency location has mixed effects with graduates from programs located within a MSA being more likely to practice solo (odds ratio 37.8) and less likely to practice in a teaching program (odds ratio .09) than the graduates from programs within a PMSA. The university affiliation of the residency only has a significant association with partnership practices. The graduates from residencies with a university affiliation are less likely to practice in a partnership (odds ratio .004) compared to graduates from residencies without a university affiliation. The age of the graduate has a significant association with solo practice with older graduates being more likely to practice solo. The odds ratio is 1.15 with a one year increase in age and increases to a maximum of 18.91 with a 21 year increase in age. The military status was the most consistently associated factor with the practice organization but the largest R square is less than .15 and over 85% of the variability in the practice organization is not due to military status of the residency.

The multiple logistic regressions for the practice location were clinically significant for all of the possible locations – rural, MSA, CMSA, PMSA, and outside the U.S. (Table 10). The regression analysis do not confirm that the military status of the residency is responsible for differences in the proportions of graduates practicing in rural locations or outside the US as had been identified in the military vs. civilian comparisons. The military status is significantly associated with the practice location only for practices within a CMSA. The civilian residency graduates are more likely to practice in a CMSA (odds ratio 13.8) than the graduates from military residencies. The other factors that are significantly associated with the practice location include the location of the residency, the type of hospital at the residency, the average preparedness for practice score, the number of hospital beds in the residency, and the university affiliation of the residency. It is noted that the R square values are less than .25 and all of these factors account for less than 25% of the variability in practice location.

The factor that is most consistently associated with the practice location is the residency location. A very interesting pattern occurs where the graduates from the least urban areas (rural or MSA) are the most likely to practice in a rural area while the graduates from the most urban areas (PMSA) are the most likely to practice in the highly urban areas (CMSAs and PMSAs). The type of hospital at the residency has mixed effects with the graduates from residencies in medical centers being more likely to practice in a MSA (odds ratio 3.2) or PMSA (odds ratio 13.5) and less likely to practice in a CMSA (odds ratio .31) compared to the graduates from residencies in community hospitals. The average preparedness for practice score also has mixed effects on the practice location. A higher preparedness score increases the likelihood of the graduate practicing outside the US (odds ratios 1.64 for a 1 point increase in average score) and decreases the likelihood of the graduate practicing within a MSA (odds ratio .72 for a 1 point increase in average score). The number of hospital beds in the residency is only associated with practice location for practices within a PMSA. An increase in the number of hospital beds increases the likelihood of the graduate practicing in a PMSA - odds ratio from 1.007 with a 1 bed increase in hospital size to a maximum of 18,034 with a 1400 bed increase. The university affiliation of the residency is only associated with the practice location for practices within a CMSA. The graduates from university affiliated and university based residencies are less likely to practice in a CMSA (odds ratios .024 and .006, respectively) compared to the graduates from residencies without a university affiliation.

Table 2 – Demographics of Military and Matched Civilian Residency Graduates

	Military Graduates (n = 161)	Civilian Graduates (n = 122)	Mann-Whitney p value	Chi Square p value
Age	32 years (median)	32 years (median)	.693
Gender	80.1% males	62.3% males	.001
Number of Residents Per Year	10 (median)	8 (median)	.917
Residency Program Age	24 years (median)	24 years (median)	.823
Number of Other Residencies
0 other residencies	44.7%	50.0%		.378
1 other residencies	14.3%	18.0%		.393
2 other residencies	4.3%	3.3%		.762
³ 3 other residencies	36.6%	28.7%		.159
Type of hospital				.041
Community Hospital	56.5%	44.3
Medical Center	43.5%	55.7
Residency Location
Rural	0	5.7%		.003
MSA	66.5%	58.2%		.154
CMSA	23.0%	4.1%		.000
PMSA	10.5%	32.0%		.000
Residency University Affiliation
No affiliation	85.1%	2.5%		.000
University affiliated Community program	14.9%	72.1%		.000
University administered Community program	0	9.0%		.000
University based program	0	16.4%		.000
Number of beds	200 (median)	470 (median)	.000

Table 3 – Subject Area Likert Scores and Inclusion Into Clinical Practice

Subject area	Mean	Median	Variance	% Including in Clinical Practice
Alcohol & Substance Abuse	6.56	7	3.75	83.0
Counseling	6.31	7	4.01	86.7
Psychiatric Disorders	6.60	7	3.27	96.3
Family Life Cycle	6.47	7	4.12	81.7
Cardiology	7.81	8	2.40	97.4
Endocrine	6.94	7	2.31	98.5
Pulmonary	7.60	8	1.94	99.3
Hematology / Oncology	6.29	7	2.88	90.6
Gastroenterology	7.84	8	1.72	99.6
Infectious Diseases	7.53	8	1.84	97.8
Rheumatology	6.20	6	3.18	93.7
Nephrology	5.88	6	2.89	85.8
Allergy and Immunology	6.57	7	3.44	95.9
Neurology	6.69	7	2.73	97.0
Women’s Health	8.20	9	2.45	97.8
Obstetrics	8.46	9	2.82	62.4
Gynecology	8.32	8	1.82	98.5
General Surgery	7.06	7	2.90	73.2
Orthopedics	7.05	7	3.62	96.3
Urology	6.90	7	2.49	94.8
Otolaryngology	7.38	8	2.33	97.8
Sports Medicine	7.20	7	3.36	96.3
Emergency Medicine	7.59	8	2.69	83.2
Pediatrics	8.07	8	1.69	95.9
Newborn Nursery	7.90	8	2.62	64.6
Community Medicine	7.16	7	3.17	78.1
Health Promotion	7.63	8	3.35	94.0
Geriatrics	7.38	8	2.63	81.9
Dermatology	7.59	8	2.00	99.6
Research & Scholarly Activity	5.34	5	5.44	28.4
Diagnostic Imaging and Nuclear Medicine	5.96	6	3.10	67.3
Personal Finance	3.86	3	5.37	64.7
Office Management	3.90	4	4.89	65.0
Personnel Management	3.77	3	5.19	76.7
Business Planning	3.35	3	4.24	51.8
Managed Care	4.66	5	6.17	84.6
Professional Liability	4.31	4	5.32	75.1
Average Score	6.61	6.59	0.99

Table 4 – Military vs. Civilian Subject Area Likert Scores and Scope of Practice

Subject Area	Military Residency Graduates		Civilian Residency Graduates		Mann- Whitney p value	Chi Square p value
	Mean (median)	% Including in Practice	Mean (median)	% Including in Practice
Alcohol & Substance Abuse	6.62 (7)	79.1	6.71 (7)	88.5	.513	.043
Counseling	6.14 (6)	87.9	6.53 (7)	85.0	.078	.483
Psychiatric Disorders	6.53 (7)	97.5	6.70 (7)	94.7	.286	.329
Family Life Cycle	6.30 (7)	78.4	6.69 (7)	86.2	.111	.107
Cardiology	7.73 (8)	96.8	7.92 (8)	98.2	.128	.703
Endocrine	6.90 (7)	98.7	6.98 (7)	98.2	.542	1.000
Pulmonary	7.48 (8)	99.4	7.76 (8)	99.1	.103	1.000
Hematology / Oncology	6.24 (7)	89.0	6.36 (6.5)	92.9	.464	.290
Gastroenterology	7.77 (8)	100	7.93 (8)	99.1	.198	.418
Infectious Diseases	7.49 (8)	98.1	7.57 (8)	97.3	.570	.697
Rheumatology	6.13 (6)	91.1	6.29 (6.5)	97.3	.621	.040
Nephrology	5.73 (6)	82.8	6.09 (6)	90.1	.069	.092
Allergy and Immunology	6.68 (7)	97.5	6.43 (7)	93.7	.338	.209
Neurology	6.78 (7)	97.5	6.57 (7)	96.4	.397	.722
Women’s Health	8.37 (9)	98.7	7.99 (8)	96.4	.075	.236
Obstetrics	9.06 (9)	75.2	7.63 (8)	44.7	.000	.000
Gynecology	8.55 (9)	98.7	8.02 (8)	98.2	.015	1.000
General Surgery	7.26 (7)	76.4	6.78 (7)	68.8	.029	.161
Orthopedics	7.48 (8)	98.1	6.48 (7)	93.8	.000	.099
Urology	7.21 (7)	96.2	6.47 (7)	92.8	.000	.225
Otolaryngology	7.43 (8)	99.4	7.31 (7)	95.5	.538	.085
Sports Medicine	7.52 (8)	97.4	6.79 (7)	94.6	.001	.329
Emergency Medicine	7.53 (8)	88.5	7.67 (8)	75.9	.460	.007
Pediatrics	8.06 (8)	94.2	8.07 (8)	98.2	.647	.128
Newborn Nursery	8.08 (8)	66.0	7.66 (8)	62.5	.123	.552
Community Medicine	6.94 (7)	77.9	7.44 (8)	78.4	.002	.929
Health Promotion	7.50 (8)	95.5	7.80 (8)	91.8	.116	.212
Geriatrics	7.23 (7)	72.7	7.58 (8)	94.6	.039	.000
Dermatology	7.67 (8)	100	7.49 (8)	99.1	.163	.415
Research & Scholarly Activity	6.01 (6)	31.8	4.43 (4)	23.4	.000	.132
Diagnostic Imaging and Nuclear Medicine	6.23 (6)	73.4	5.59 (6)	58.7	.003	.013
Personal Finance	3.74 (3)	53.9	4.02 (4)	80.2	.352	.000
Office Management	3.69 (3)	64.3	4.18 (4)	66.0	.085	.771
Personnel Management	3.79 (3)	79.1	3.74 (3)	73.3	.863	.283
Business Planning	3.14 (3)	46.1	3.63 (3)	60.0	.055	.028
Managed Care	4.29 (4)	78.4	5.15 (5)	93.5	.005	.001
Professional Liability	3.96 (4)	64.2	4.77 (5)	90.6	.004	.000
Average Score	6.63 (6.6)		6.58 (6.6)		.795

Table 5 – Practice Characteristics of Military and Civilian Residency Graduates

	Military Graduates (n = 161)	Civilian Graduates (n = 122)	Mann-Whitney p value	Chi Square p value
Board certification	96.3%	95.9%		1.000
Practice Organization
Solo practice	1.3%	12.3%		.000
Partnership of two physicians	1.3%	8.2%		.004
Single-specialty group of 3 or more physicians	36.5%	41.0%		.338
Multi-specialty group	30.1%	20.5%		.096
Teaching	12.2%	5.7%		.080
Emergency department	0	1.6%		.185
Inpatient care	0	0.8%		.431
Other	18.6%	9.8%		.053
Practice Location
Rural	18.8%	31.1%		.016
MSA	38.8%	43.4%		.427
CMSA	13.1%	17.2%		.339
PMSA	3.8%	8.2%		.110
Outside the US	25.6%	0		.000
Time Distribution of Work Activities
Patient care	75% (median)	85% (median)	.000
Administrative work	15% (median)	10% (median)	.000
CME	5% (median)	5% (median)	.797
Teaching	5% (median)	0.5% (median)	.174
Other	0% (median)	0% (median)	.066
Average Patient Visits per Week	90 (median)	100 (median)	.002

Table 6 – Significant Preparedness for Practice Multiple Linear Regressions

	Significant Independent Variables	b	Significance (t or Partial F)	Adjusted R square	F test significance
Obstetrics preparation score				.232	.000
	Gender (women)	.552	.009
	Military status (civilian residency)	-1.415	.000
	Number of other residencies present		< .05
	One	-.628	.031
	Two	1.066	.042
	Three or more	-.248	.347
Gynecology preparation score				.140	.000
	Gender (women)	.641	.000
	Number of other residencies present		< .01
	One	-.656	.008
	Two	1.281	.004
	Three or more	.205	.355
Orthopedics preparation score				.100	.000
	Number of other residencies present		< .05
	One	.104	.768
	Two	1.183	.066
	Three or more	.790	.015
Office Management preparation score				.160	.000
	Age	-.084	.008
	Residency location		< .01
	MSA	-2.638	.002
	CMSA	-1.839	.049
	PMSA	-1.715	.049
	Residency age	-.088	.001
	Number of beds	.002	.008
Personnel Management preparation score				.107	.000
	Gender (women)	-.858	.006
	Age	-.073	.029
	Residency location		< .05
	MSA	-1.984	.031
	CMSA	-.972	.327
	PMSA	-1.347	.145
	Residency age	-.086	.003
	Number of beds	.0017	.032
Business Planning preparation score				.106	.000
	Residency age	-.071	.007
	Number of beds	.0019	.010

Table 7 – Scope of Practice Multiple Logistic Regressions (Subject Areas Not Practiced

By At Least 25% of The Physicians)

	Significant Independent Variables	Odds Ratio (eb )	b	significance	R square
Obstetrics					.276
	Military status (civilian residency)	.089	-2.417	.004
	Residency location			.012
	Rural	.169	-1.775	.080
	MSA	.116	-2.157	.001
	CMSA	.167	-1.789	.016
	Preparedness score	1.491	.399	.001
General Surgery					.219
	Military status (civilian residency)	.135	-1.999	.013
	Residency location			.098
	Rural	.726	-.321	.775
	MSA	1.818	.598	.403
	CMSA	9.498	2.251	.027
	Residency university affiliation			.038
	Univ affiliated	7.274	1.984	.012
	Univ administered	6.945	1.938	.108
	Univ based	26.478	3.276	.005
	Preparedness score	1.825	.602	.000
Newborn Nursery					.186
	Graduation year	.435	-.832	.007
	Residency location			.011
	Rural	.104	-2.264	.043
	MSA	.134	-2.011	.002
	CMSA	.250	-1.388	.067
	Number of other residencies present			.029
	One	.272	-1.303	.004
	Two	1.427	.356	.766
	Three or more	.964	-.037	.934
	Practice location			.113
	MSA	.530	-.634	.129
	CMSA	.478	-.738	.194
	PMSA	.251	-1.381	.082
	Outside the US	.257	-1.358	.010
Research and Scholarly Activity					.234
	Residency location			.029
	Rural	.100	-2.305	.082
	MSA	.140	-1.964	.004
	CMSA	.175	-1.746	.031
	Preparedness score	1.717	.541	.000

Table 7 - Continued

	Significant Independent Variables	Odds Ratio (eb )	b	significance	R square
Diagnostic Imaging & Nuclear Medicine					.279
	Military status (civilian residency)	.142	-1.955	.027
	Residency university affiliation			.066
	Univ affiliated	5.76	1.751	.030
	Univ administered	.854	-.157	.900
	Univ based	3.401	1.224	.273
	Preparedness score	2.212	.794	.000
Personal Finance					.206
	Gender (women)	.416	-.876	.017
	Graduation year	1.889	.636	.044
	Number of residents per year	.845	-.168	.039
	Number of other residencies present			.209
	One	.378	-.974	.042
	Two	.996	-.004	.997
	Three or more	.989	-.011	.981
	Preparedness score	1.185	.169	.026
Office Management					.109
	Number of other residencies present			.112
	One	.368	-.999	.028
	Two	2.227	.801	.424
	Three or more	.926	-.077	.871
Business Planning					.120
	Age	1.125	.117	.005

Table 8 – Scope of Practice Multiple Logistic Regressions (Subject Areas Not Practiced

By 10-25% of The Physicians)

	Significant Independent Variables	Odds Ratio (eb )	b	significance	R square
Alcohol and Substance Abuse					.166
	Practice location			.050
	MSA	.183	-1.696	.006
	CMSA	.292	-1.232	.117
	PMSA	.396	-.927	.478
	Outside the U.S.	.590	-.528	.491
	Preparedness score	1.286	.252	.018
Counseling					.127
	Residency university affiliation			.082
	Univ affiliated	.298	-1.211	.191
	Univ administered	.048	-3.038	.017
	Univ based	.645	-.438	.776
	Preparedness score	1.318	.276	.008
Family Life Cycle					.215
	Graduation year	.244	-1.411	.001
	Residency location			.045
	Rural	.121	-2.116	.174
	MSA	.075	-2.594	.005
	CMSA	.082	-2.501	.016
	Practice location			.057
	MSA	.228	-1.478	.012
	CMSA	.230	-1.468	.047
	PMSA	.210	-1.563	.175
	Outside the U.S.	.834	-.182	.816
	Preparedness score	1.735	.551	.000
Nephrology					.179
	Graduation year	.356	-1.034	.015
	Residency age	1.114	.108	.023
	Practice location			.063
	MSA	.197	-1.623	.023
	CMSA	.295	-1.220	.160
	PMSA	.256	-1.361	.330
	Outside the U.S.	.103	-2.271	.004
	Preparedness score	1.449	.371	.005
Emergency Medicine					.140
	Gender (women)	.431	-.842	.043
	Number of residents per year	.734	-.309	.006
	Preparedness score	1.270	.240	.036
Community Medicine					.184
	Number of other residencies present			.031
	One	.681	-.384	.478
	Two	2.001	.693	.631
	Three or more	.245	-1.409	.004
	Preparedness score	1.734	.551	.000

Table 8 - Continued

	Significant Independent Variables	Odds Ratio (eb )	b	significance	R square
Geriatrics					.180
	Number of other residencies present			.155
	One	.298	-1.210	.038
	Two	.126	-2.070	.204
	Three or more	.547	-.603	.364
Personnel Management					.131
	Age	1.137	.129	.022
	Preparedness score	1.358	.306	.001
Managed Care					.108
	None			All > .05
Professional Liability					.212
	Age	1.187	.171	.006
	Military status (civilian residency)	6.316	1.843	.043
	Residency age	1.149	.139	.002
	Preparedness score	1.266	.236	.006

Table 9 – Significant Practice Organization Multiple Logistic Regressions

	Significant Independent Variables	Odds Ratio (eb )	b	significance	R square
Solo Practice					.143
	Respondent Age	1.150	.140	.021
	Military status (civilian residency)	29.701	3.391	.029
	Residency location			.080
	Rural	9.688	2.271	.132
	MSA	37.850	3.634	.011
	CMSA	6.831	1.921	.241
Partnership of two physicians					.104
	Military status (civilian residency)	8027.43	8.991	.001
	Residency university affiliation			.046
	Univ affiliated	.004	-5.425	.006
	Univ administered	.000	-13.070	.778
	Univ based	.004	-5.579	.012
Practice in a Teaching Program					.121
	Military status (civilian residency)	.022	-3.813	.007
	Residency location			.094
	Rural	.003	-5.968	.867
	MSA	.090	-2.408	.028
	CMSA	.251	-1.383	.276

Table 10 – Significant Practice Location Multiple Logistic Regressions

	Significant Independent Variables	Odds Ratio (eb )	b	significance	R square
Practice in rural location					.140
	Residency location			.041
	Rural	17.70	2.873	.017
	MSA	3.14	1.146	.041
	CMSA	2.07	.727	.312
Practice in a MSA					.175
	Residency location			.000
	Rural	2.044	.715	.556
	MSA	8.376	2.125	.000
	CMSA	6.777	1.914	.002
	Type of hospital at the residency (medical center)	3.217	1.169	.001
	Avg. preparedness for practice score	.717	-.333	.019
Practice in a CMSA					.189
	Military status (civilian residency)	13.819	2.626	.013
	Residency location			.001
	Rural	.000	-8.933	.687
	MSA	.018	-4.005	.000
	CMSA	.060	-2.810	.004
	Type of hospital at the residency (medical center)	.314	-1.159	.038
	Residency university affiliation			.015
	Univ affiliated	.024	-3.739	.002
	Univ administered	.000	-8.415	.638
	Univ based	.006	-5.080	.002
Practice in a PMSA					.174
	Residency location			.065
	Rural	.001	-7.326	.938
	MSA	.007	-5.005	.007
	CMSA	.309	-1.175	.492
	Type of hospital at the residency (medical center)	13.459	2.600	.038
	Number of beds	1.007	.007	.018
Practice outside the US					.227
	Avg. preparedness for practice score	1.635	.492	.026

Discussion

This survey found an overall level of good preparation for clinical practice with the military and civilian residency graduates feeling well prepared in at least thirty of the thirty-seven subject areas of ACGME required instruction. The practice management disciplines of personal finance, office management, personnel management, business planning, managed care and professional liability were the only subject areas where poor preparation for clinical practice was present. The regression analysis did not identify a causative factor and the military and civilian residency graduates were equally poorly prepared. This poor preparation is of concern as all of the prior studies also indicate that the training in practice management is inadequate. It is noted that the ACGME curriculum requires 60 hours of instruction in practice management. One possible cause of the poor preparation would be that 60 hours is an insufficient amount of time to teach or learn such a complex field. Another possibility would be that the residents do not recognize the importance of practice management and place a low priority on learning this material compared to the patient care related subjects. The high preparedness scores in the clinical subjects in conjunction with the low preparedness scores in practice management in this survey support this hypothesis. A final possibility would be that the residencies are doing a poor job of teaching the material and may use their 60 hours of practice management instruction to teach material that is not important. A future study would be valuable to determine whether the residents, the residencies, or the amount of time dedicated to teaching practice management is responsible for the poor preparation in practice management.

The military and civilian residency graduates have incorporated most of the thirty-seven subject areas of ACGME required instruction into their clinical practice. The teaching of these subjects during residency training is mandated by the ACGME because they are believed to be central to the specialty of Family Practice. In this survey, there were eight subjects which are not practiced by at least 25% of the physicians - Obstetrics, General Surgery, Newborn Nursery, Research and Scholarly Activity, Diagnostic Imaging and Nuclear Medicine, Personal Finance, Office Management, and Business Planning. It is possible that the ACGME is incorrect and these subject areas are not essential to the practice of family medicine. Research and Scholarly Activity is an excellent example of a subject area which the ACGME, American Academy of Family Physicians, and American Board of Family Practice want family physicians to practice but is seldom adopted. The military also emphasizes Research and Scholarly Activity with 11 of its 14 military residencies requiring a research or scholarly activity project (compared to only 3 of the 37 civilian residencies in this survey), but this emphasis did not produce a greater incorporation of Research and Scholarly Activity into the practice of military residency graduates. In addition to a residency graduate electing not to include a subject area in his/her medical practice, the medical system he or she works in may preclude the practice of a subject area. It cannot be determined in this survey if the graduates are electing not to perform a subject area or if external constraints are precluding the practice of the subject area.

The military status of the residency accounts for few significant differences in the preparedness for practice scores, scope of practice, or the size of the community where the graduates establish their medical practice. The military residency graduates are significantly more likely than the civilian residency graduates to include Obstetrics, General Surgery, and Diagnostic Imaging and Nuclear Medicine in their clinical practice but less likely to include Professional Liability. The differences in Obstetrics, Diagnostic Imaging and Nuclear Medicine, and Professional Liability are probably a result of the military medical system and not the residency training as military family physicians are generally expected to practice Obstetrics, perform the initial reading on their X rays, and have little involvement with the malpractice coverage which is provided by the government. Although the preparation for practice and scope of practice results in this study are similar for military and civilian residency graduates, this should not be interpreted to mean that a civilian residency graduate would feel just as well prepared in a military family practice or conversely that a military residency graduate would feel just as well prepared in a civilian family practice. The clinical and administrative practices of military and civilian family physicians are different and the military and civilian residencies are doing equally well in preparing their graduates for the clinical practice they will enter.

The military status of the residency is associated with significant differences in the distribution of work time and the type of practice established. Military residency graduates spend a larger proportion of their work time in administrative work and a smaller proportion in patient care with less patient visits per week compared to the graduates from civilian residencies. The 10% less of the work time in patient care matches the 10% fewer patients for the military physicians. The amount of time dedicated to each activity is unknown as the survey queried the distribution of work time and not the number of hours worked per week. Without knowing how much time was spent in patient care, it is not possible to assess the clinical efficiency in patient encounters. The civilian residency graduates in this survey had an average of 100 patient visits per week which is very similar to the average of 101.6 for Family Practice residency graduates in a 1995 national survey.¹ The civilian graduates are more likely to practice solo or in a two physician partnership but this is probably a result of the military medical system and not the residency training. While the civilians have relatively free choice, the military physicians have a limited ability to choose the location of their practice, practice organization, or scope of practice and there are few solo or two physician partnership practices in the military.

The most notable finding of this study is that many resident, community, and residency program characteristics are significantly associated with a Family Practice residency graduate’s preparedness for practice, scope of practice, and other practice characteristics but they account for less than one third of the outcome variability. These factors include gender, respondent age, graduation year, military status of the residency, residency location, type of hospital at the residency, university affiliation of the residency, residency program age, number of residents per year, number of other residencies present, the number of hospital beds at the residency, practice location, and the preparedness for practice score in the subject area.

A few interesting patterns were identified. A trend toward greater use of group practice may be occurring as over 60% of the military and civilian graduates are in a single specialty of multi-specialty group practice while in a 1995 national survey of family physicians, only 50% were in group practices. ¹ The graduates from residencies located in rural areas are the most likely to establish their clinical practice in a rural area. The graduates from residencies in the most urbanized areas (primary metropolitan statistical areas) are the most likely to establish their clinical practice in highly urbanized areas (consolidated metropolitan statistical areas or primary metropolitan statistical areas). Approximately sixty percent of the physicians in this study established their practice in a metropolitan area and the percentage is similar to that found in the Geyman et al. analysis of Minnesota, Virginia, and Washington residency program graduates.¹⁵

The finding that a higher average preparedness score increases the likelihood of practicing outside the United States is interesting and reflects a social consciousness on the part of military. The military physicians are the only ones practicing outside the US and many of the overseas assignments in the military are involuntary. The overseas physicians are typically located in clinics or small community hospitals which have less subspecialty support than in is present in the United States. As a result, the community has a greater reliance on their family physicians. Although not an official military policy, the military healthcare planners have sought to assign physicians who are well prepared for these more demanding assignments.

The principal limitation of this study is the use of self reported data to measure preparedness for practice, scope of practice, and the other practice characteristics. It would be ideal to use a validated objective tool to measure how well a residency graduate is prepared for clinical practice but no such tool exists. Instead, one must rely on the graduate’s perception of how well he or she was prepared for clinical practice as a result of residency training. Measurement errors will occur whenever the graduate under or overestimates how well he or she is prepared. Even with the possible measurement error, the self perceived degree of preparation is an important measure. The past studies also used the perceived degree of preparation to assess the adequacy of residency training and its use in this survey enables comparisons to those results. In addition, this study found that the preparedness score is the most consistently associated factor with whether or not a subject area is incorporated into clinical practice. A subject area is more likely to be performed if the graduate feels well prepared.

The use of self reported data is also a potential source of error in the scope of practice measures. The survey instrument did not provide definitions of what constitutes practice of a subject area and employed a "yes" or "no" answer on whether the subject is included in the clinical practice. The lack of definitions may have been an issue in the practice management disciplines where the low preparedness scores raises a concern that the physicians may have a poor understanding of what these subjects encompass. A problem of this nature was not detected in the pretesting of the survey instrument, but this does not exclude the possibility. The provision of standard definitions of what constitutes practice would be helpful in future studies but is not possible as the ACGME does not provide a description or definition for the practice management subjects. A final consideration is that the survey did not assess whether the graduates are electing not to perform a subject area or external constraints are precluding their practice of the subject.

The use of self reported data also introduces measurement errors when assessing practice characteristics such as the number of patient visits per week or the distribution of work time. The use of objective measurements would greatly reduce the measurement errors but would be difficult and very expensive to obtain as time studies would have to be done in approximately 300 different practice locations.

The important learning points from this study are that it is not practical to try to match two groups of Family Practice residency graduates and that it is almost pointless to perform simple comparative analysis between the two different groups. This study began with the idea that several resident, community, and residency program characteristics would significantly affect a Family Practice residency graduate’s preparedness for practice, scope of practice, and practice characteristics. An extensive effort to match military and civilian residency graduates was successful in five of the six targeted criteria but significant differences were present in four other characteristics that were also associated with the outcomes of interest. Although many significant differences were present in the comparisons of military and civilian residency graduates' preparedness for practice and scope of practice, few were confirmed in the regression analyses. Instead, the regression analysis found that numerous resident, community, and residency program characteristics are significantly associated with the preparation for practice and scope of practice. In future studies, it would be advisable to purchase a list family practice residency graduate names, select the study subjects with stratified random sampling for the factor of interest (i.e. male vs. female), and employ multiple linear and logistic regressions in the data analysis to control for the other factors that are associated with the outcome.

Conclusion

This survey found good preparation for clinical practice in the graduates of matched military and civilian residency programs with most of the subjects areas of ACGME required instruction incorporated into their clinical practice. Poor preparation for clinical practice was reported in the practice management disciplines of personal finance, office management, personnel management, business planning, managed care and professional liability. This poor preparation is of concern as it has been identified in all of the prior studies of Family Practice residency graduates and the practice management disciplines are performed by the majority of these residency graduates in their clinical practice.

The military status of the residency accounts for few significant differences in the preparedness for practice scores or scope of practice and does not influence the size of the community where the graduates establish their practice. The military status of the residency is associated with significant differences in the distribution of time spent in work activities and the type of practice established. Given the high expectation for military family physicians to perform administrative duties, the finding that they spend a 14% smaller proportion of their work time in patient care and a 13% larger proportion in administrative work compared to civilian residency graduates is not surprising. Group practices are the most common practice arrangement and the lower likelihood for military physicians to practice solo or in a two physician partnership is due to a lack of availability of these practices in the military.

The most notable finding of this study is that many resident, community, and residency program characteristics are significantly associated with a Family Practice residency graduate’s preparedness for practice, scope of practice, and other practice characteristics but they account for less than one third of the outcome variability. These factors include gender, respondent age, graduation year, military status of the residency, residency location, type of hospital at the residency, university affiliation of the residency, residency program age, number of residents per year, number of other residencies present, the number of hospital beds at the residency, practice location, and the preparedness for practice score in the subject area. A high degree of correlation is present between these factors which makes it nearly impossible to change one factor without affecting several others. Simple comparisons of residency graduates should not be performed without matching or controlling for these resident, community, and residency program characteristics.

Citations

1. American Academy of Family Physicians, 1996 Facts About Family Practice, Kansas City, American Academy of Family Physicians, 1996, Tables 7, 8, 10, 13, 21.
2. Bradshaw DM, Miser WF, "Non-Clinical Roles of Army Family Physicians in Their First Post-Residency Assignment and Their Level of Preparedness for These Roles," Military Medicine, Vol 161, Sep. 1996, pp. 547-51.
3. Blount BW, Hart LG, Ehreth JL, "A Comparison of the Content of Army Family Practice With Nonfederal Family Practice," Journal of the American Board of Family Practice, Vol 7(5), Sep.-Oct. 1994, pp. 395-402.
4. Hecht RC, Farrell JG, "Graduate Follow-Up in the University of Wisconsin Family Practice Residency Programs," Journal of Family Practice, Vol 14(3), Mar. 1982, pp. 549-555.
5. Fitzhugh M, Wood M, Marsland DW, et al., "Graduate Follow-Up in the Medical College of Virginia/Virginia Commonwealth University Family Practice Residency System," Journal of Family Practice, Vol 11(5), 1980, pp. 731-42.
6. Ciriacy EW, Bland CJ, Stoller JE, et al., "Graduate Follow-Up in the University of Minnesota Affiliated Hospitals Residency Training Program in Family Practice and Community Health," Journal of Family Practice, Vol 11(5), 1980, pp. 719-30.
7. Geyman JP, Cherkin DC, Deisher JB, et al., "Graduate Follow-Up in the University of Washington Family Practice Residency Network," Journal of Family Practice, Vol 11(5), 1980, pp. 743-52.
8. LeClair BM, Blount BW, Miser WF, et al., "Army Family Practice: Does Our Training Meet Our Needs?" Military Medicine, Vol 162, Sep. 1997, pp. 601-4.
9. Cantor JC, Baker LC, Hughes RG, "Preparedness for Practice – Young Physicians’ Views of Their Professional Education," JAMA, Vol 270(9), Sep. 1993, pp. 1035-1040.
10. Bredfeldt RC, Proffitt DL, Wesley RM, "A Comparison of Resident Perceptions of Training Experience at University and Community Based Family Practice Residencies," Family Medicine, Vol 22(6), Nov.-Dec. 1990, pp. 434-6.
11. Ellsbury K, Schneeweiss R, Montano DE, et al., "Gender Differences in Practice Characteristics of Graduates of Family Medicine Residencies," Journal of Medical Education, Vol 62(11), Nov. 1987, pp. 895-903.
12. Gwyther RE, Bentz EJ, Marquardt M, et al., "Practice Trends Among Graduates of Two Family Practice Residency Programs in North Carolina," North Carolina Medical Journal, Vol 51(12), Dec. 1990, pp. 653-7.
13. Davidson RC, Kahn NB, "A Comparison of University-Based and Community-Based Family Practice Residency Programs," Journal of Family Practice, Vol 18(4), 1984, pp. 581-6.
14. Barclay AM, Knapp DP, Kallail KJ, "The Provision of Labor and Delivery Services by Graduates of Four Kansas Family Practice Residencies," Kansas Medicine, Vol 97(1), Spring 1996, pp. 19-23.
15. Geyman JP, Ciriacy EW, Mayo F, et al., "Geographic Distribution of Family Practice Residency Graduates: The Experience of Three Statewide Networks," Journal of Family Practice, Vol 11(5), 1980, pp. 761-6.
16. American Academy of Family Physicians, American Academy of Family Physicians 1995 Directory of Family Practice Residency Programs, Kansas City; American Academy of Family Physicians, 1995.
17. American Academy of Family Physicians, American Academy of Family Physicians 1996 Directory of Family Practice Residency Programs, Kansas City; American Academy of Family Physicians, 1996.
18. American Academy of Family Physicians, American Academy of Family Physicians 1997 Directory of Family Practice Residency Programs, Kansas City; American Academy of Family Physicians, 1997.
19. American Medical Association, The AMA Directory of Physicians In The United States, 38^th ed., Chicago; American Medical Association, 1999, Volume 1.
20. American Board of Medical Specialties, The Official ABMS Directory of Board Certified Medical Specialists, 31^st ed, New Providence; Marquis Who’s Who, 1999, Volume 1.

Bibliography

American Academy of Family Physicians, 1996 Facts About Family Practice, Kansas City, American Academy of Family Physicians, 1996.

American Academy of Family Physicians, American Academy of Family Physicians 1995 Directory of Family Practice Residency Programs, Kansas City; American Academy of Family Physicians, 1995.

American Academy of Family Physicians, American Academy of Family Physicians 1996 Directory of Family Practice Residency Programs, Kansas City; American Academy of Family Physicians, 1996.

American Academy of Family Physicians, American Academy of Family Physicians 1997 Directory of Family Practice Residency Programs, Kansas City; American Academy of Family Physicians, 1997.

American Board of Medical Specialties, The Official ABMS Directory of Board Certified Medical Specialists, 31^st ed, New Providence; Marquis Who’s Who, 1999, Volume 1.

American Medical Association, The AMA Directory of Physicians In The United States, 38^th ed., Chicago; American Medical Association, 1999, Volume 1.

Barclay AM, Knapp DP, Kallail KJ, "The Provision of Labor and Delivery Services by Graduates of Four Kansas Family Practice Residencies," Kansas Medicine, Vol 97(1), Spring 1996, pp. 19-23.

Blount BW, Hart LG, Ehreth JL, "A Comparison of the Content of Army Family Practice With Nonfederal Family Practice," Journal of the American Board of Family Practice, Vol 7(5), Sep.-Oct. 1994, pp. 395-402.

Bradshaw DM, Miser WF, "Non-Clinical Roles of Army Family Physicians in Their First Post-Residency Assignment and Their Level of Preparedness for These Roles," Military Medicine, Vol 161, Sep. 1996, pp. 547-51.

Bredfeldt RC, Proffitt DL, Wesley RM, "A Comparison of Resident Perceptions of Training Experience at University and Community Based Family Practice Residencies," Family Medicine, Vol 22(6), Nov.-Dec. 1990, pp. 434-6.

Brennan M, Stewart M, "Attitudes and Patterns of Practice: A Comparison of Graduates of a Residency Program in Family Medicine and Controls," Journal of Family Practice, Vol 7(4), Oct.

1978, pp. 741-8.

Cable TA, Delaney MJ, "A Graduate Survey of the Relevance of a Family Practice Residency Curriculum," North Carolina Medical Journal, Vol 56(8), Aug. 1995, pp. 360-3.

Cantor JC, Baker LC, Hughes RG, "Preparedness for Practice – Young Physicians’ Views of Their Professional Education," JAMA, Vol 270(9), Sep. 1993, pp. 1035-1040.

Ciriacy EW, Bland CJ, Stoller JE, et al., "Graduate Follow-Up in the University of Minnesota Affiliated Hospitals Residency Training Program in Family Practice and Community Health," Journal of Family Practice, Vol 11(5), 1980, pp. 719-30.

Davidson RC, Kahn NB, "A Comparison of University-Based and Community-Based Family Practice Residency Programs," Journal of Family Practice, Vol 18(4), 1984, pp. 581-6.

Ellsbury K, Schneeweiss R, Montano DE, et al., "Gender Differences in Practice Characteristics of Graduates of Family Medicine Residencies," Journal of Medical Education, Vol 62(11), Nov. 1987, pp. 895-903.

Fitzhugh M, Wood M, Marsland DW, et al., "Graduate Follow-Up in the Medical College of Virginia/Virginia Commonwealth University Family Practice Residency System," Journal of Family Practice, Vol 11(5), 1980, pp. 731-42.

Forbes RC, Walley E, "Graduate Follow-Up in the University of Mississippi Family Practice Residency Program," Journal of the Mississippi State Medical Association, Vol 33(5), May 1992, pp. 171-4.

Geyman JP, Cherkin DC, Deisher JB, et al., "Graduate Follow-Up in the University of Washington Family Practice Residency Network," Journal of Family Practice, Vol 11(5), 1980, pp. 743-52.

Geyman JP, Ciriacy EW, Mayo F, et al., "Geographic Distribution of Family Practice Residency Graduates: The Experience of Three Statewide Networks," Journal of Family Practice, Vol 11(5), 1980, pp. 761-6.

Gwyther RE, Bentz EJ, Marquardt M, et al., "Practice Trends Among Graduates of Two Family Practice Residency Programs in North Carolina," North Carolina Medical Journal, Vol 51(12), Dec. 1990, pp. 653-7.

Hecht RC, Farrell JG, "Graduate Follow-Up in the University of Wisconsin Family Practice Residency Programs," Journal of Family Practice, Vol 14(3), Mar. 1982, pp. 549-555.

Jones GN, Rees AC, "Lafayette’s Family Practice Residency Program: Practice Patterns of Graduates," Journal of the Louisiana State Medical Society, Vol 148(8), Aug. 1996, pp. 359-63.

LeClair BM, Blount BW, Miser WF, et al., "Army Family Practice: Does Our Training Meet Our Needs?" Military Medicine, Vol 162, Sep. 1997, pp. 601-4.

McCranie EW, Hornsby JL, Calvert JC, "Practice and Career Satisfaction Among Residency Trained Family Physicians: A National Survey," Journal of Family Practice, Vol 14(6), Jun. 1982, pp. 1107-14.

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Appendix A – Survey Questionnaire

An Assessment of Family Medicine Residency Training

1. Which ONE of the following best describes your practice organization? (Check only one)

_____ Solo practice

_____ Partnership of two physicians

_____ Single-specialty group of three or more physicians

_____ Multi-specialty group

_____ Teaching program

_____ Emergency Department

_____ Inpatient care

_____ Other – please describe ________________________________

2. What is the zip code where your practice is located? ______________

3. Using 5% increments indicate the percentage of your work time that you spend in each activity?

_____ Patient care

_____ Administrative work

_____ CME

_____ Teaching

_____ Other - please describe ________________________________

(Total = 100%)

4. On average how many patient visits do you have per week? _________

5. Circle the response which best indicates how well your residency prepared you to provide

patient care in each subject area. Also indicate whether or not you include the subject area in your practice.

(For example the evaluation of anemia would constitute practice of Hematology and care of congestive heart failure would constitute practice of Cardiology)

	Very Poorly Prepared									Very Well Prepared		Part of your practice?
Alcohol / substance abuse		1	2	3	4	5	6	7	8	9	10	yes	no
Counseling		1	2	3	4	5	6	7	8	9	10	yes	no
Psychiatric disorders		1	2	3	4	5	6	7	8	9	10	yes	no
Family life cycle		1	2	3	4	5	6	7	8	9	10	yes	no
Cardiology		1	2	3	4	5	6	7	8	9	10	yes	no
Endocrine		1	2	3	4	5	6	7	8	9	10	yes	no
Pulmonary		1	2	3	4	5	6	7	8	9	10	yes	no
Hematology / Oncology		1	2	3	4	5	6	7	8	9	10	yes	no
Gastroenterology		1	2	3	4	5	6	7	8	9	10	yes	no
Infectious disease		1	2	3	4	5	6	7	8	9	10	yes	no
	(Continued On Other Side)
	Very Poorly Prepared									Very Well Prepared		Part of your practice?
Rheumatology		1	2	3	4	5	6	7	8	9	10	yes	no
Nephrology		1	2	3	4	5	6	7	8	9	10	yes	no
Allergy and Immunology		1	2	3	4	5	6	7	8	9	10	yes	no
Neurology		1	2	3	4	5	6	7	8	9	10	yes	no
Women’s Health		1	2	3	4	5	6	7	8	9	10	yes	no
Obstetrics		1	2	3	4	5	6	7	8	9	10	yes	no
Gynecology		1	2	3	4	5	6	7	8	9	10	yes	no
Surgery		1	2	3	4	5