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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".