Creating a ‘Health Promoting Curriculum’ to inform the development of a Health Promoting University: a case study
Bibliographic record
Abstract
Objective: Final year undergraduate students, undertaking a health promotion module, were asked to reflect on their experiences of contributing to the strategic development of a health promoting university. As part of this module students were engaged in carrying out a health needs assessment (HNA) in order to inform the development of a health promoting university and enhance the curriculum. Design: This case study was conducted with forty students, and used un-moderated focus groups (n = 9) where students recorded their experiences of carrying out a HNA. Results: Key findings of the students' reflections (rather than of the health needs assessment) are presented in this paper. Students reported that by carrying out a HNA they: developed an understanding of the links between theory and practice; developed communication and networking skills; found the assessment meaningful; welcomed the opportunity to make a difference; could identify improvements that could have been made, along with challenges to carrying out their work and health gains. Conclusion: This study has shown how the student learning experience has been enhanced, whilst at the same time the university (and potentially the wider community) will potentially benefit from the HNA that was carried out. Ultimately, the students were able to ‘reach the parts that other researchers can't reach’, i.e. explore complex issues amongst their fellow peers.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".