Undergraduate training in geriatric medicine: getting it right
Bibliographic record
Abstract
It is pehaps inevitable that every specialty within medicine believes that its fundamental principles should be included in the core undergraduate curriculum and should be accompanied by a compulsory placement. The General Medical Council's (GMC) policy document ‘Tomorrow's Doctors’ [ 1 ] stresses the importance of students learning about the special problems associated with older people's health. For example, it states that young doctors must respect age and the vulnerability of particular patient groups including older people. They also emphasise that graduates must understand human development, which includes growing old. Importantly, the document states that students must have opportunities to interact with a range of people including visiting an older person, a learning experience that is now included in many curricula. However, they are silent on the issue of whether there should be a compulsory attachment to a geriatric medicine unit, as indeed they are, about other hospital specialties. The present policy suggests that a quarter to a third of the curriculum should be based on student-selected components. The interpretation and implementation of such advice will be different in each medical school but the risk is, that at this time of demographic change, with growing numbers of older people, and with other demands on the curriculum, exposure to geriatric medicine might be overlooked and reduced.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.062 | 0.041 |
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".