The <i>Climate Wise</i> slides: An evaluation of planetary health lecture slides for medical education
Bibliographic record
Abstract
There is an urgent need for innovations in planetary health medical education. Physicians must be prepared to provide high-value, low-carbon healthcare for patients increasingly impacted by the health consequences of climate change. The Climate Wise slides, an evidence-based, open-access pedagogical tool that provides didactic planetary health medical education organized by medical subspecialty, was developed and evaluated by a virtual lecture session that presented a subset of the slides to N = 75 Canadian medical students. Each participant completed a questionnaire before and after the Climate Wise virtual lecture that included multiple choice questions to assess their planetary health knowledge and a rating of their interest in including the Climate Wise slides in medical curricula. Participants showed significantly improved planetary health knowledge scores (p < 0.0001) and increased interest in including the Climate Wise slides in medical curricula (p < 0.001) after the virtual Climate Wise lecture session. This study demonstrates that the Climate Wise slides are a valuable pedagogical tool to advance planetary health medical education. Future directions include evaluating faculty perspectives on the Climate Wise slides, learning outcomes of the slides implemented longitudinally in medical curricula, and developing higher-order problem-based and simulation-based planetary health medical education resources. Given the urgent need for planetary health medical education, we recommend the global sharing of teaching resources to facilitate the rapid upscaling of validated pedagogical tools internationally.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".