Family Medicine Mandatory Assessment of Progress Results of a pilot administration of a family medicine competency-based in-training examination
Bibliographic record
Abstract
OBJECTIVE: To report the results of a pilot in-training progress test, the Family Medicine Mandatory Assessment of Progress, taken by first- and second-year postgraduate family medicine trainees. DESIGN: Assessment of resident performance on a key-features approach multiple-choice progress test. Test questions were developed by competency content area experts. SETTING: University of Toronto in Ontario. PARTICIPANTS: First- and second-year family medicine residents. MAIN OUTCOME MEASURES: Construct validity was assessed based on performance on the test by first- and second-year residents, Canadian and international medical graduates, and residents with more or less than 1 month of relevant clinical experience. RESULTS: Pilot progress testing of family medicine residents (N = 255) at the University of Toronto revealed a significant 1.6% difference (P < .01) in mean scores between first- and second-year postgraduate family medicine trainees and achieved construct validity across many parameters studied. The agreement coefficients for residents being identified as the poorest performers ranged from 0.88 to 0.90 depending on the domain of practice assessed. CONCLUSION: Competency-based progress testing using the key-features model is a valid means of assessing the progress of family medicine residents.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".