The Medical Education Planetary Health Journey: Advancing the Agenda in the Health Professions Requires Eco-Ethical Leadership and Inclusive Collaboration
Bibliographic record
Abstract
Climate change and the declining state of the planet’s ecosystems, due mainly to a global resource-driven economy and the consumptive lifestyles of the wealthy, are impacting the health and well-being of all Earth’s inhabitants. Although ‘planetary health’ was coined in 1980, it was only in the early 2000s that a call came for a paradigm shift in medical education to include the impact of ecosystem destabilization and the increasing prevalence of vector-borne diseases. The medical education response was, however, slow, with the sustainable healthcare and climate change educational agenda driven by passionate academics and clinicians. In response, from about 2016, medical students have taken action, developing much-needed learning outcomes, resources, policies, frameworks, and an institutional audit tool. While the initial medical education focus was climate change and sustainable healthcare, more recently, with wider collaboration and engagement (Indigenous voices, students, other health professions, community), there is now planetary health momentum. This chronological account of the evolution of planetary health in medical education draws on the extant literature and our (an academic, students, and recent graduates) personal experiences and interactions. Advancing this urgent educational agenda, however, requires universities to support inclusive transdisciplinary collaboration among academics, students and communities, many of whom are already champions and eco-ethical leaders, to ensure a just and sustainable future for all of Earth’s inhabitants.
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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 | 0.034 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.032 |
| Scholarly communication | 0.028 | 0.032 |
| Open science | 0.003 | 0.035 |
| Research integrity | 0.017 | 0.038 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".