Bibliographic record
Abstract
History shows that curriculum review has brought progressive change to undergraduate medical content, structure and strategic direction, but in recent times there has been escalation of change in response to the overcrowded medical curriculum, workforce shortages and credentialing issues. Current global trends include: problem-based learning replacing an older emphasis on didactic specialty lectures; increased community-based compared to a focus on hospital-based experience; more self-directed learning; inter-professional teaching and learning; and particular emphasis on the structure, nature and timing of clinical skills training. Our Faculty of Medicine funded a project on international perspectives on undergraduate clinical skills teaching and learning to inform such change. In our consultation project, a senior team of four multidisciplinary academic staff set out to collate peer-reviewed literature, including medical education conference proceedings, and to consult with international key informants (20 clinical skill tutors and programme managers, mostly from UK, Canada, USA, Australia and New Zealand) to identify the issues of primary importance to clinical skills teaching and learning and to develop a framework for further debate. This information was then discussed with local medical education consumers (our medical students) and producers (educationalists, clinicians and their patients) and key informants reviewed the final report before the web-based dissemination (Moriarty et al 2006) was launched.
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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.075 |
| Scholarly communication | 0.024 | 0.036 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.012 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".