FM-MAP: A Novel In-Training Examination Predicts Success on Family Medicine Certification Examination.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The objective of our study was to assess the correlation between a locally developed In-Training Examination (ITE) and the certification examination in family medicine in Canada. METHODS: The ITE was taken twice yearly, which corresponded for most residents to the fifth, ninth, 17th, and 21st month of training. The results for the ITE were correlated to the CFPC certification examination taken in the 23rd month of residency. RESULTS: The scores on each of the four iterations of the ITE correlated moderately well with performance relating to problem solving skills and knowledge on the certification examination. The ITE showed a trend to an increased correlation with duration in the training program with a Spearman correlation coefficient increasing from 0.45 on the first test to 0.54 on the fourth test. The correlation of the ITE with performance on the component assessing the doctor- patient relationship on the certification examination was poor (r=0.26 on the last test). CONCLUSION: Our in-training examination is a useful predictor of performance in problem solving and knowledge domains of the family medicine expert role on the certification examination.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".