Assessing the impact of virtual learning on family medicine trainees’ medical knowledge using progress tests: a retrospective cohort study
Bibliographic record
Abstract
Background: Progress testing provides residents with an opportunity to identify strengths and weaknesses, encouraging self-directed learning. The University of Toronto's Department of Family & Community Medicine administers the Family Medicine Mandatory Assessment of Progress (FM-MAP) biannually to track resident competency and medical knowledge. Our aim was to determine the impact of virtual learning on Family Medicine residents. Methods: We administered previous iterations of the FM-MAP to the virtual learning cohort and compared scores to those of the in-person cohort between October 2020 - Spring 2022. Results: There were no statistically significant differences between in-person and virtual cohorts of first- and second-year postgraduate family medicine trainees regarding their overall FM-MAP score. Second-year family medicine trainees outperformed first year trainees in both cohorts. Conclusion: The study found no significant effect on the scores of first- and second-year family medicine trainees caused by the shift to virtual learning, suggesting medical curricula can incorporate virtual learning without compromising trainee progress, offering flexibility in medical education. Future studies could explore its applicability across different residency programs and long-term effects on clinical performance.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".