The Medical Education Planetary Health Journey: Advancing the Agenda in the Health Professions Requires Eco-Ethical Leadership and Inclusive Collaboration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it