A qualitative study of what motivates and enables climate-engaged physicians in Canada to engage in health-care sustainability, advocacy, and action
Bibliographic record
Abstract
Increasing numbers of health-care professionals are aware of the need to deliver low-carbon sustainable health systems. We aimed to explore how physicians can be motivated and supported to pursue this ambition by conducting an exploratory qualitative descriptive study that involved individual in-depth interviews with climate-engaged Canadian physicians participating in health-care sustainability advocacy and action. Interview transcripts were analysed to identify themes related to the actions that physicians can take to promote sustainable health care, and the motivators and enablers of physician engagement in sustainable health care. Participants (n=19) engaged in a spectrum of health-care sustainability initiatives ranging from reducing health-care waste to lobbying and political action. They were motivated to advance health-care sustainability by their concern about the health implications of climate change, frustration with health-care waste, and recognition of their locus of influence as physicians. Participants articulated that policy and system, organisational and team, and knowledge generation and translation supports are required to strengthen their capacity to advance health-care sustainability. These findings can provide inspiration for engagement opportunities in health-care sustainability, guide service delivery and educational innovations to promote health-care professionals' interest in becoming sustainability champions, and extend the capacity of health-care professionals to reduce the climate impact of health care.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".