Assessing the Impact of a Geriatric Clinical Skills Day on Medical Students’ Attitudes Toward Geriatrics
Bibliographic record
Abstract
BACKGROUND: The aging population requires an improvement in physicians' attitudes, knowledge, and skills, regardless of their specialty. This study aimed to identify attitude changes of University of Toronto pre-clerkship medical students towards geriatrics after participation in a Geriatric Clinical Skills Day (GCSD). METHODS: This was a before and after study. The GCSD consisted of one large and four small interactive, inter-professional geriatric medicine workshops facilitated by various health professionals. A questionnaire, including the validated UCLA Geriatrics Attitudes Scale, was administered to participating pre-clerkship medical students before and after the GCSD. A one-sample t-test and signed rank parametric test were used to determine attitude changes. RESULTS: 42.1% indicated an interest in Geriatric Medicine, 26.3% in Geriatric Psychiatry, and 63.2% in working with elderly patients. Both pre- and post-mean scores were greater than 3 (neutral), indicating a positive attitude before and after the intervention (p < .001). There was no significant difference in the change in mean total scores (signed rank test p ≥ .12, Student's t-test p > .11). CONCLUSIONS: The GCSD did not alter pre-clerkship students' attitudes towards geriatrics. This study adds to geriatric medical education research and warrants further investigation in a larger, multi-centred trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".