Engaging medical students and their teachers with the determinants of health: the approaches and impact of a curriculum development at one large UK medical school
Bibliographic record
Abstract
Background: Social determinants of health (‘SDH’) often underlie the health behaviours that contribute to non-communicable disease. Doctors need to be aware of health behaviours and their determinants and the evidence-based interventions to address them. Aim: Co-ordinated core curriculum modifications were instigated with explicit learning outcomes in and around health promotion and SDH. This paper reports on the research evaluation of process and outcome and sustainability of these changes in one large medical school. Method: Mixed method research data were used to inform an action research cycle. Data were analysed for content and emerging themes related to smoking cessation, obesity reduction, and global health were informed by SDH. Results and analysis: Students demonstrated knowledge and concern relating to SDH, although some initially lacked confidence in applying this knowledge. Students reported inconsistent modelling clinical environments. Attention was given to the learning environment as well as teacher training to facilitate and support self-efficacy through reflection and critical analysis. Conclusion: Newer medical education themes such as SDH need robust preparation for inclusion in core curricula, with attention to the social, cognitive and environmental impacts on learning. Teaching and experiential learning for SDH is now embedded in this curriculum.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".