Integrating occupational and public health sciences through a cross-national educational partnership
Bibliographic record
Abstract
Complex social issues, sometimes referred to as ‘wicked problems’, influence the conditions of everyday life and the occupations these conditions afford, which are key determinants of health and well-being. Education is an important arena through which social transformation of oppressive conditions can be promoted and enacted. At the same time, interdisciplinary approaches have been recognized as being essential for addressing ‘wicked problems’. We argue that linking occupational science and public health in education is a fruitful way forward for understanding complex issues and enacting social change. Our purpose is to describe a partnership between universities in Norway and Canada in order to address how the integration of occupational and public health perspectives on diverse health determinants contributed to the interdisciplinary education and mentorship of future researchers and health care practitioners. Three specific examples are addressed; the participation of students from each country in courses at the institutions abroad; the development of integrated public health and occupation-based curriculum materials; and the undertaking of interdisciplinary research conducted by graduate students that was co-supervised by occupational science and public health scholars from both countries. The cross-national educational partnership has contributed to the enhancement of participating students’ education, as well as the expansion of the partnership itself. Authors’ reflections regarding factors contributing to the success of the partnership, and challenges associated with sustaining it over time, are also briefly addressed. The description of the partnership articulates how international and interdisciplinary collaboration in education can expand the reach and potential impacts of occupation-based knowledge.
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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 | 0.027 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.046 |
| Research integrity | 0.005 | 0.010 |
| 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".