New horizons in undergraduate geriatric medicine education
Bibliographic record
Abstract
Current projections show that between 2000 and 2050, increasing proportions of older individuals will be cared for by a smaller number of healthcare workers, which will exacerbate the existing challenges faced by those who support this patient demographic. This review of a collection of Age and Ageing papers on the topic in the past 10 years explores (1) what best practice geriatrics education is and (2) how careers in geriatrics could be made more appealing to improve recruitment and retention. Based on these deeper understandings, we consider, as clinician educators, how to close the gap both pragmatically and theoretically. We point out paradigm shifting solutions that include innovations at the Undergraduate level, use of simulation, incorporation of learner and patient perspectives, upskilling professionals outside of Geriatrics and integration of practice across disciplines through Interprofessional Learning. We also identify an education research methodological gap. Specifically, there is an abundance of simple descriptive or justification studies but few clarification education studies; the latter are essential to develop fresh insights into how Undergraduate students can learn more effectively to meet the needs of the global ageing challenge. A case of improving understanding in delirium education is presented as an illustrative example of a new approach to exploring at greater depth education and outlines suggested directions for the future.
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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".