Regulation of Health Professionals’ Work as a Climate Mitigation Strategy: Opportunities, Responsibilities, and Challenges
Bibliographic record
Abstract
Background: The climate impacts of health professionals’ work are significant. The potential role and opportunities for regulators of health professionals’ work to drive behavioural and practice change have not been adequately explored in the literature. The objective of this research was to examine regulators’ perspectives on the potential role of health professions’ regulatory bodies in advancing the adoption of climate-conscious professional practice. Methods: Semi-structured interviews with 19 regulators overseeing the practice of health professionals in medicine, nursing, pharmacy, and dentistry in Canada were undertaken. Constant comparative data analysis using nVivo v15 was undertaken to identify common themes. The COREQ framework was applied to ensure the quality of the research processes used. Results: Participants highlighted their belief that there are only limited opportunities for health professions’ regulators to lead climate-positive practice change, despite their personal beliefs in the importance of the topic. The use of educational approaches, rather than legal or regulatory tools, was emphasized. Concerns were raised regarding regulatory overreach, practitioner blowback, and practical/logistical considerations. Coalition building across different facets of a profession (including educational institutions, unions, workplaces, and professional/advocacy groups) was identified as potentially most impactful. Conclusions: Previous research had highlighted practitioners’ beliefs that regulators had significant legal and practice-directed levers that could drive behavioural change towards more climate-friendly health care work. This research has highlighted regulators’ discomfort with assuming a legalistic role. Instead, they favoured persuasive techniques such as education and coalition building that may nudge, rather than compel, practitioners towards more climate-friendly practice.
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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 | 0.086 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.029 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".