Exploring faculty perspectives toward developing a planetary health curriculum for family medicine residents at the University of Toronto
Bibliographic record
Abstract
Background: Climate change is the greatest threat to human health of this century, yet limited formal curriculum exists within postgraduate family medicine (FM) programs across Canada. As outlined by The College of Family Physicians of Canada (CFPC) Guides for Improvement of Family Medicine Training (GIFT) report, learners have called for planetary health (including climate change) education and recommended a curriculum framework. This study aimed to understand University of Toronto Department of Family Medicine faculty attitudes around implementing a planetary health curriculum within the FM residency program. Methods: This study used a qualitative descriptive design. Thirty faculty members from various teaching, curriculum, and leadership positions were invited to participate in virtual semi-structured video interviews. Data was collected and analyzed using thematic analysis. Results: Thirteen interviews were conducted between May-September 2022. Participants perceived planetary health was relevant to FM, but most were unfamiliar with the term. Four overarching themes were developed from the data: (1) curriculum implementation, (2) curriculum development, (3) barriers, and (4) attitudes. Barriers to integrating PH learning objectives include a lack of faculty knowledge and skills, burnout, and an already saturated FM curriculum. Conclusion: To address the climate crisis, there is need for a planetary health curriculum, yet faculty have a limited understanding of this topic. This knowledge gap is one of multiple barriers to curriculum implementation this study identified. This study provides insight and suggestions for tools that may aid planetary health curriculum development and implementation.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".