Equipping tomorrow's doctors for the patients of today
Bibliographic record
Abstract
As the proportion of older patients with frailty presenting to health services increases, so does the need for doctors to be adequately trained to meet their needs. The presentations seen in such patients, the evidence-based models of care and skillsets required to deliver them are different than for younger patient groups-so specific training is required. Several research programmes have used detailed and explicit methods to establish evidence-based expert-validated curricula outlining learning outcomes for undergraduates in geriatric medicine-there is now broad-consensus on what newly qualified doctors need to know. There are, despite this, shortcomings in the teaching of undergraduates about geriatric medicine. National and international surveys from the UK, EU, USA, Canada, Austria and the Netherlands have all shown shortcomings in the content and amount of undergraduate teaching. Mechanisms to improve this situation, aside from specifying curricula, include developing academic departments and professorships in geriatric medicine, providing grants to develop teaching in geriatric medicine and developing novel teaching interventions to make the best of existing resources. Under the last of these headings, innovations have been shown to improve outcomes by: using technology to ensure the most effective allocation of teaching time and resources; using inter-professional education as a means of improving attitudes towards care of older patients; focusing teaching specifically on attitudes towards older patients and those who work with them; and trying to engage patients in teaching. Research areas going forward include how to incentivise medical schools to deliver specified curricula, how to choose from an ever-expanding array of teaching technologies, how to implement interprofessional education in a sustainable way and how to design teaching interventions using a qualitative understanding of attitudes towards older patients and the teams that care for them.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.012 |
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".