Performance on the American Board of Family Medicine Certification Examination by Country of Medical Training
Bibliographic record
Abstract
BACKGROUND: Performance on the American Board of Family Medicine (ABFM) Certification and Recertification Examinations by country of medical school training has not been examined. Based on internal medicine patterns, we hypothesize that examinees trained in the United States and Canada would outperform examinees trained in other countries. METHODS: In this retrospective cohort study from 2004 to 2011, data on the ABFM examinations were obtained from the ABFM. Fisher exact and χ(2) tests were performed across years based on the country of examinee training. Simple linear regression was performed to evaluate pass rates over time. All statistics were performed using an α = 0.05. RESULTS: The overall pass rate over the study period was 84.4% (74,821 of 88,680). The pass rate for US medical graduates (USMGs) was 88.3% (60,328 of 68,332). The pass rate for Canadian medical graduates (CMGs) was 93.8% (872 of 930). The pass rate for non-Canadian foreign medical gradates (NC-FMGs) was 70.1% (13,621 of 19,418). CMGs had a higher pass rate than USMGs (P < .001) and NC-FMGs (P < .001). Simple linear regression showed significant decreasing trends over time for all examinees (P = .02), for USMGs (P = .02), and for CMGs (P = .02). CONCLUSIONS: USMGs and CMGs outperform NC-FMGs on the ABFM certification and recertification examinations. These findings may alter acceptance patterns for Family Medicine residency programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".