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Biomedical subjects

J B Erdmann

Publications and source records attributed to J B Erdmann.

17 recordsLinked to original sources
PubMed1995

Primary care and non-primary care physicians: a longitudinal study of their similarities, differences, and correlates before, during, and after medical school.

PURPOSE--To investigate similarities and differences between physicians in primary care and non-primary care specialties on performance measures prior to, during, and after medical school, and on demographic characteristics, professional plans and preferences in medical school, professional activities, career satisfaction, perceived problems and research activities, and to predict primary--non-primary care career choices from information obtained in medical school. A questionnaire was mailed to 1,076 physicians who graduated from Jefferson Medical College between 1982 and 1986. Of those who responded (62%), 232 were primary care and 406 were non-primary care physicians (29 physicians in mixed specialties were excluded). Data from the questionnaire concerning professional activities, satisfaction, problems, and research productivities were merged with the college's longitudinal study database. RESULTS--Comparisons of primary care--non-primary care physicians indicated no significant difference between them on performance measures before, during, and after medical school, with the exception that non-primary care physicians had higher scores on quantitative tests before medical school, and primary care physicians scored higher on a licensing examination of general clinical skills and patient management taken during residency training. Also, compared with non-primary care physicians, those in primary care were less likely to be employed full-time, were less likely to locate in metropolitan areas, had a lower rate of academic appointment, and had a higher rate of board certification. Other results showed differences between the groups in terms of age at entrance to medical school, proportion of women, estimates during medical school of anticipated income, career plans during medical school, satisfaction with career and income, and research and scientific activities. A logistic regression model could predict primary care--non-primary care status from specialty interest, professional plans and interests expressed in medical school.

Attitude of Health Personnel↗
PubMed1995

A model for predicting HEAL repayment patterns and its implications for medical student financing.

BACKGROUND: The current level of Health Education Assistance Loan (HEAL) defaults has exceeded the original estimate, and as such is producing an unforeseen federal expenditure. Predicting repayment patterns of HEAL borrowers is an important step in assessing the impact that this unforeseen expenditure will have on HEAL and other financial aid programs. METHOD: Since prior research on educational lending has not identified a practical model for predicting repayment patterns, the authors turned in 1994 to an alternative discipline, research on consumer lending. Using the multivariate discriminant analysis credit scoring model framework, the authors incorporated operational definitions of the borrower's character, capacity, and capital. To identify factors that are significant in establishing repayment category membership, the framework was applied to a group of 233 HEAL borrowers who graduated from five medical schools in 1988. Because of the small numbers of delinquent borrowers and defaulters, the repayment categories were restricted to repayment, deferment, and forbearance. RESULTS: The level of unsubsidized debt, in conjunction with financial resources (including the potential resource of parental support), may be significant in identifying those HEAL borrowers who may confront repayment difficulty. CONCLUSION: While the results are not surprising, they do lend documented support for a review of financial aid policies at both institutional and governmental levels, and a framework within which this review may be conducted.

Education, Medical, Undergraduate↗
PubMed1993

Background essential to the proper use of results of step 1 and step 2 of the USMLE.

This first of the four-part set of articles published in this issue provides general information and concepts about the Step 1 and Step 2 examinations of the United States Medical Licensing Examination (USMLE) needed for the proper use of these tests' results in three general, related, non-licensure applications: for the evaluation of the examinees' levels of academic achievement, for the evaluation of educational programs the examinees have experienced, and for the selection of examinees into residency programs. Several aspects of the tests are discussed: (1) their original and continuing purpose (which is to assess certain qualifications required for licensure of physicians); (2) their content; (3) their format and the emphases (relative to knowledge or skills testing) that different formats can give, and the concept that the ways the same content areas are formatted in a test will produce different results; (4) test administration, with a discussion of "speeded" versus "power" tests and the organization of test items by difficulty and by (or not by) topic; (5) reliability, with a discussion of standard error and the importance of understanding measurement error in order to discern real differences in scores; (6) scoring, with definition of anchor scores; and (7) interpretation, with discussions of pass/fail criteria in the past, present, and future of the USMLE tests. The authors close by saying that to interpret test performance properly, it is important not only to bear in mind the ideas in the present article but also to carefully review an actual test or at least a representative sample of test questions.

Educational Measurement↗
PubMed1993

Use of the USMLE to select residents.

Many studies have examined the relationships between students' performances on the National Board of Medical Examiners (NBME) Part I and Part II examinations and their postgraduate clinical performances. Most studies have found a positive relationship between students' NBME examination scores and the ratings of residents' clinical performances and/or scores on specialty board certification examinations. Surveys of residency directors have also shown that NBME scores are used as part of the process for selecting residents, although other data and other selection criteria are considered more useful than the NBME scores. One area of continued concern is that a large body of data on the United States Medical Licensing Examination (USMLE) is not yet available. However, the predictive validity of the NBME scores supports the use of USMLE scores as part of the review process to screen potential residents. Residency directors should continue to use a variety of criteria in their final selection decisions and are encouraged to use their own program-specific data to establish and monitor particular cutoff scores for screening applicants.

Clinical Competence↗
PubMed1992

Comparing the accuracies of entire-group and subgroup models to predict NBME-I scores for medical school applicants.

To address the question of whether prediction models for subgroups of medical school applicants lead to more accurate predictions of performance than does one model for an entire group of applicants, the authors used data from two groups of students at Jefferson Medical College: 415 students who entered Jefferson in 1985 and 1986, and 396 who entered in 1987 and 1988. Both groups were divided into two subgroups by gender and two subgroups by age. Data from the first group were used to develop prediction models based on the entire group and on its four subgroups. The predictors were undergraduate grade-point averages and Medical College Admission Test scores; the criterion measures were scores on the National Board of Medical Examiners Part I examinations. The prediction models were then applied to data from the second group and its four subgroups: differences in the validity coefficients (.40 to .56) and residual scores (7.2 to 17.9) were not considered to be of practical importance. Hence, the authors suggest that gender and age do not contribute to a prediction bias and that an entire-group prediction model can be used without serious concern for over-or underestimating the predicted scores.

Adult↗
PubMed1980

Validating the MCAT.

Explore the source record for details and available documents.

Educational Measurement↗
PubMed1975

Personality dimensions as assessed by projective and verbal instruments.

An attempt was made to explore personality dimensions with projective and verbal tests. The Holtzman Inkblot Technique (HIT), the California Psychological Inventory (CPI) and the Rokeach Dogmatism Scale (RDS) were administered to 161 college students of both sexes. A description of the canonical correlations between the two subsets of projective and verbal instruments is presented, as well as three separate factor analyses, one of the HIT, another of the CPI-RDS and the third of the HIT and CPI-RDS together. The results support the conclusion that, with the exception of one factor that includes HIT and CPI-RDS variables, the HIT factors have no relationship with the CPI-RDS factors. Furthermore, of the 19 canonical correlations only the first is significant.

Adolescent↗