Comparing academic performance of medical students in distributed learning sites: the McMaster experience
Bibliographic record
Abstract
BACKGROUND: In 2004, the Michael G. DeGroote School of Medicine at McMaster University in Hamilton, Ontario, developed the McMaster Community and Rural Education program (Mac-CARE), to coordinate core rotations for undergraduate and post-graduate medical learners in communities in Southern Ontario. AIMS: The purpose of this study is to compare the academic performance of medical clerks learning at distributed sites to students who remained in Hamilton using four measures of academic performance. METHODS: Progress test, OSCE, clerkship scores, and pre-clerkship tutorial-based evaluations were collected and Mac-CARE students were compared to non-Mac-CARE students on each performance measure using ANOVA. RESULTS: Outcomes are based on the first cohort to engage in Mac-CARE rotations. There were no statistically significant differences in academic performance between the 2 groups before the intervention rotation (pre-clerkship and clerkship evaluations, progress tests, or an inaugural OSCE). Mac-CARE students, however, scored higher on their post-clerkship OSCE than did non-Mac-CARE students. CONCLUSION: This study has shown that academic performance among students was at least comparable across all learning sites. To our knowledge, this is the first such study to be published within a Canadian context.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".