Pre-Clerkship Observerships to Increase Early Exposure to Geriatric Medicine
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To foster interest in geriatric care, the Queen's Geriatrics Interest Group (QGIG) collaborated with the Division of Geriatric Medicine to arrange a Geriatrics Pre-Clerkship Observership Program. METHODS: Forty-two pre-clerkship medical students participated in the program between October 2013 and May 2014. Participants were paired with a resident and/or attending physician for a four-hour weekend observership on an inpatient geriatric rehabilitation unit. The program was assessed using: (1) internally developed Likert scales assessing student's experiences and interest in geriatric medicine before and after the observership; (2) University of California Los Angeles-Geriatric Attitudes Scale (UCLA-GAS); and (3) narrative feedback. RESULTS: All participants found the process of setting up the observership easy. Some 72.7% described the observership experience as leading to positive changes in their attitude toward geriatric medicine and 54.5% felt that it stimulated their interest in the specialty. No statistically significant change in UCLA-GAS scores was detected (mean score pre- versus post-observership: 3.5 ± 0.5 versus 3.7 ± 0.4; p=.35). All participants agreed that the program should continue, and 90% stated that they would participate again. CONCLUSIONS: The observership program was positively received by students. Structured pre-clerkship observerships may be a feasible method for increasing exposure to geriatric medicine.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".