A postgraduate diploma course in community geriatrics for primary care doctors: experience of first three years
Bibliographic record
Abstract
Summary This paper describes the setting up of a postgraduate diploma course in Community Geriatrics for primary care physicians and the experience gained in its first three years of running. This study programme was set up in response to the rapidly rising elderly population in Hong Kong and the fact that most of the primary care doctors practising today had an inadequate undergraduate curriculum in the health care issues that are relevant to older people. The objectives of the Course are to improve the knowledge, skills and confidence of primary care physicians in the care of elderly people. It also emphasises the aspects of care that are unique to elderly people. The Course is delivered by different modes of learning: distance learning, face-to-face problem-orientated seminars and small group clinical teaching. Learning centres are established in different regional hospitals in Hong Kong in order to allow small group clinical teaching while, at the same time, reducing travelling time for the students. Information technology is also used to facilitate teaching and learning, as well as to encourage communication among teachers and students. The Course was oversubscribed for all its intakes in the first three years of running. Some graduates have taken on visiting medical officer positions at elderly homes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".