Planetary Health Rounds: A novel educational model for integrating healthcare sustainability education into postgraduate medical curricula
Bibliographic record
Abstract
Introduction: Climate change poses a major threat to public health, necessitating significant reductions in greenhouse gas (GHG) emissions to limit its effects. The healthcare sector itself is a significant contributor to GHG emissions, particularly in high-income countries such as Canada and the United States. Providing medical learners with education on this topic has been identified as an important component of efforts to reduce GHG emissions; however, there is a lack of tools available both for providing education on healthcare sustainability, and for integrating this topic into postgraduate medical curricula. Case Presentation: The Planetary Health Rounds are an educational initiative aimed at integrating climate change concepts and healthcare sustainability into the Internal Medicine residency curriculum, using a case-analysis format in conjunction with the open-source HealthcareLCA Database (https://healthcarelca.com/database), a living repository of data on healthcare-associated GHG emissions. Methods: Learners conduct a case analysis of an internal medicine patient and estimate the total emissions associated with their admission, which they then present at an end-of-rotation teaching session, with discussions centering on the link between climate change and health as well as reducing emissions. Discussion: The Planetary Health Rounds, implemented in 2023, have been well-received by trainee physicians despite some challenges having been encountered. These include service demands impacting participation, a lack of emissions data for internal medicine-related care, issues with the generalizability of said data, and consistent access to a planetary health expertise during rounds. Conclusion: This initiative provides a novel way of incorporating teaching on climate change and health into postgraduate training curriculums.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".