The ecological footprint of physicians: A survey of physicians in Canada, India, and USA
Bibliographic record
Abstract
Combating climate change may be the greatest public health opportunity of the 21st century. While physicians play an important role in addressing climate change, given their affluence in society, they may be an important source of greenhouse gas emissions themselves. We sought to examine the size and nature of the ecological footprint of physicians and medical students. We conducted an online survey from December 2021-May 2022 examining resource consumption, changes in consumption patterns over time, and beliefs about climate change. Participants were medical students, residents, and staff physicians in Canada, India, or USA. Only 20 out of 162 valid respondents had a low ecological footprint (12%), defined as meat intake ≤2 times per week, living in an apartment or condominium, and using public transport, bicycle, motorcycle or walking to work. 14 of these 20 participants were from India. 91% of participants were open to reducing their own ecological footprint, though only 40% had made changes in that regard. 49% participants who discussed climate change at work and at home had decreased their ecological footprint, compared to 29% of participants who rarely engaged in such conversations (OR 2.39, 95% CI 1.24-4.63, P = 0.01). We conclude that physicians have a large ecological footprint, especially those from Canada and USA. A majority of physicians are interested in reducing their ecological footprint, and those who engage in conversations around climate change are more likely to have done so. Talking frequently about climate change, at work and at home, will likely increase climate change action amongst physicians.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".