Views of health professionals on climate change and health: a multinational survey study
Bibliographic record
Abstract
Climate change arguably represents one of the greatest global health threats of our time. Health professionals can advocate for global efforts to reduce emissions and protect people from climate change; however, evidence of their willingness to do so remains scarce. In this Viewpoint, we report findings from a large, multinational survey of health professionals (n=4654) that examined their views of climate change as a human health issue. Consistent with previous research, participants in this survey largely understood that climate change is happening and is caused by humans, viewed climate change as an important and growing cause of health harm in their country, and felt a responsibility to educate the public and policymakers about the problem. Despite their high levels of commitment to engaging in education and advocacy on the issue, many survey participants indicated that a range of personal, professional, and societal barriers impede them from doing so, with time constraints being the most widely reported barrier. However, participants say various resources-continuing professional education, communication training, patient education materials, policy statements, action alerts, and guidance on how to make health-care workplaces sustainable-can help to address those barriers. We offer recommendations on how to strengthen and support health professional education and advocacy activities to address the human health challenges of climate change.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".