Adapting the Planetary Health Report Card for Graduate Medical Training Programs
Bibliographic record
Abstract
Background Leading medical organizations recognize climate change as an urgent threat to public health and social justice. Medical students created the Planetary Health Report Card (PHRC) to evaluate and spur climate action in medical schools. Graduate medical trainees lack a similar tool to evaluate and improve their training programs and institutions. Objective To adapt the PHRC to graduate medical education (GME) contexts and report preliminary validity evidence. Methods In 2023, based on literature review, we adapted the 2022 undergraduate medical PHRC metrics on curriculum and sustainability. We modified keywords in all PHRC domains to apply to GME. We recruited participants with expertise in planetary health, sustainability, and health equity affiliated with GME. Using a modified Delphi Panel method, we surveyed participants on adapted metric validity. We determined percent agreement among participants. Results We recruited 45 eligible participants, of whom 20 (44%) completed a first-round survey. Participants included a senior medical student, residents, fellows, faculty, and program directors from the United States, Canada, and the United Kingdom. Participants had a high level of agreement on metrics in the domains of curriculum, support for trainee-led initiatives, and sustainability. Some metrics in research and community engagement domains fell below the agreement threshold. Conclusions In the first round of a modified Delphi Panel survey, trainees and faculty agreed that metrics adapted from the PHRC are relevant to evaluating GME programs on planetary health, sustainability, and environmental justice.
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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 | 0.093 | 0.206 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".