Bibliographic record
Abstract
To the Editor: Over the last several decades, it has become evident that the biomedical model of health is insufficient for providing equitable care that considers the structural determinants of health and well-being. To deliver patient-centered care, medical students are now being taught the complex interplays between health outcomes, social hierarchies, and various forms of oppression. However, despite the World Health Organization describing climate change as the greatest global health threat of this century, 1 medical school curricula often do not encompass any teachings in this area. Without an appreciation for climate justice, medical students will be unprepared to support patients whose ill health is driven by the oppressive systems underlying climate change. Greenhouse gas emissions have numerous downstream impacts on the environment, the global economy, and human health, which disproportionately affect vulnerable populations such as children, those in racial/ethnic minority groups, and lower-income communities including many in the Global South. 2 According to a 2019 analysis, the health care sector was responsible for 4.4% of annual global carbon emissions. 3 As medicine is firmly grounded in the principle of nonmaleficence, neglecting to address the climate crisis is both unconscionable and paradoxically violates one of the core ethical principles of medicine. To address this gap in medical education, we partnered with the Centre for Sustainable Health Systems at the University of Toronto to host a 6-part webinar series and certificate program on sustainability in medicine. This series had more than 270 registered participants from 8 Canadian provinces. Medical students were well represented among participants, engaging on topics ranging from sustainable practices during a pandemic to advocacy approaches for climate action, and many participants shared ways in which they are advancing the global climate action agenda at their own institutions. Furthermore, a Canadian medical school took notice of our efforts and asked for our assistance in implementing sustainable medicine discussion points in their undergraduate medical curriculum. We are invigorated by the positive response from our colleagues and hope that further trainee-led efforts will continue to inspire ambitious action to build a more equitable future for all on local, national, and international stages. As medical students, we have the power to create change in academic medicine. Throughout our medical school journey, we hope to use our newfound privilege as members of the medical profession to further raise awareness of the health inequities associated with climate change and to better advocate for climate justice. Acknowledgments: The authors thank those at the Centre for Sustainable Health Systems for their guidance and support throughout this process. They also thank Rebecca Wang, a medical student at Temerty Faculty of Medicine, for her assistance in developing the webinar series and certificate program.
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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 | 0.014 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.030 | 0.024 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".