Predicting Family Medicine Specialty Certification Status Using Standardized Measures
Bibliographic record
Abstract
One of the routes for entry into practice for international medical graduates (IMGs) in Canada entails completing some form of an in-practice assessment program. The latter route is referred to as practice ready assessment and is the focus of the present investigation.A pan-Canadian practice ready assessment process is currently being designed to evaluate IMGs' practice readiness. The selection of candidates who will not only have the highest likelihood of successfully completing the practice-ready assessment program but who will also attain specialty certification is of paramount importance. Our study focused on assessing how well practice-ready assessment candidates' performance on Medical Council of Canada (MCC) examinations and four demographic variables could predict both their score and pass fail status on the College of Family Physicians' (CFPC) certification examination.Data from 132 practice-ready assessment candidates were analyzed and indicate that MCC Qualifying Examination Part 1 scores, gender and age were significant predictors of both pass/fail status (p<0.05) as well as scores (p<0.01) on the short-answer management problems component of the family medicine certification examination.This study provides initial validity evidence for using the MCCQE Part I as a selection tool for practice-ready assessment. Practice-ready assessment programs across Canada might consider adopting the set of standardized predictors examined in this investigation, in addition to other measures, in an effort to better promote a pan-Canadian model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".