Are medical educators following General Medical Council guidelines on obesity education: if not why not?
Bibliographic record
Abstract
BACKGROUND: Although the United Kingdom's (UK's) General Medical Council (GMC) recommends that graduating medical students are competent to discuss obesity and behaviour change with patients, it is difficult to integrate this education into existing curricula, and clinicians report being unprepared to support patients needing obesity management in practice. We therefore aimed to identify factors influencing the integration of obesity management education within medical schools. METHODS: Twenty-seven UK and Irish medical school educators participated in semi-structured interviews. Grounded theory principles informed data collection and analysis. Themes emerging directly from the dataset illustrated key challenges for educators and informed several suggested solutions. RESULTS: Factors influencing obesity management education included: 1) Diverse and opportunistic learning and teaching, 2) Variable support for including obesity education within undergraduate medical programmes, and 3) Student engagement in obesity management education. Findings suggest several practical solutions to identified challenges including clarifying recommended educational agendas; improving access to content-specific guidelines; and implementing student engagement strategies. CONCLUSIONS: Students' educational experiences differ due to diverse interpretations of GMC guidelines, educators' perceptions of available support for, and student interest in obesity management education. Findings inform the development of potential solutions to these challenges which may be tested further empirically.
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 | 0.020 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".