Development and Evaluation of a Mandatory Course in Geriatric Medicine for Fourth Year Medical Students
Bibliographic record
Abstract
Background and Objectives: As the population ages, older adults will make up an increasing proportion of the practices of most physicians. Because of this, education of medical students in Geriatric Medicine is essential, yet there is considerable variability in the amount, timing within the curriculum, and content of geriatric training in Medical Schools. Our goal was to develop and evaluate an integrated, mandatory 3-week geriatric medicine course for fourth year medical students with emphasis on knowledge acquisition. Methods: All fourth year medical students at Dalhousie Medical School underwent 2 days of didactic teaching on core geriatric topics and a 2-week clinical rotation. Pre-rotation knowledge testing occurred on the first day of the rotation. On the final examination, students were retested on the 15 pre-rotation questions, as well as 5 additional questions that they had not encountered previously. Results: There was a statistically significant improvement in examination performance from 46.9 % on the pretest to 78.6 % on the final examination (t=24.7, p<.001). It is unlikely that the significant improvement in scores is simply a result of repeat testing, as students tended to score better on the five additional questions that they had not seen before.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".