Health humanities for inclusive, globally interdependent, supportive and decolonised health professional education: The future is health humanities!
Bibliographic record
Abstract
To find its place in healthcare that is responsive to global determinants of health and adaptively shape healthcare systems, health professional education (HPE) requires deep and central engagement with arts, culture and the health humanities. In this paper, we overview the trajectory of health humanities and the centrality of humanities scholarship in grappling with coloniality and power as ongoing features of health and healthcare. We then discuss current research that asks how arts and humanities can best be incorporated in HPE. Drawing from a recent Worldwide Universities Network initiative, we set out a framework comprising six domains of learning and 11 graduate capabilities that can be used to implement and evaluate health humanities education. Health humanities offers an invitation for imaginative and joyful innovations in HPE over the next 50 years.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.042 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.001 | 0.024 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".