“But I See Old People Everywhere”: Dispelling the Myth That Eldercare Is Learned in Nongeriatric Clerkships
Bibliographic record
Abstract
PURPOSE: To test the assumption that knowledge, attitudes, and skills (KAS) in geriatrics are learned via exposure to elderly patients in nongeriatric clerkships. In the developed world, the proportion of adults > or = 65 years old will soon surpass the proportion of children <14. However, clinical clerkships containing geriatric rotations are not mandated by the Liaison Committee for Medical Education. METHOD: The authors assessed differences in geriatrics-focused KAS between medical students who completed a rotation in eldercare and those who completed a traditional nongeriatric clerkship. Over two academic years, the authors randomly assigned 263 clinical clerks to a clerkship year that did (eldercare group) or did not contain a two-week rotation focused on geriatrics. All students completed questionnaires that assessed their knowledge of and attitudes toward geriatric patients before and after their clerkships. Before graduation, all students completed an objective structured clinical examination (OSCE) including a clinical station focused on geriatrics. RESULTS: Questionnaire and OSCE station response rates were 74.8% and 100%, respectively. The eldercare group had significantly higher knowledge scores (P = .004). Students' attitudes toward older adults worsened over the clerkship year in both groups, but slightly less in the eldercare group; that group had significantly higher OSCE geriatric station scores and overall pass rates (both: P < .001). CONCLUSIONS: Geriatrics is often regarded as a nonessential discipline. This study showed, however, that a clerkship year containing a specialized geriatric rotation is significantly more effective than a traditional clerkship year in preparing students to care for an aging population.
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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 | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".