Education for the Anthropocene: Planetary health, sustainable health care, and the health workforce
Bibliographic record
Abstract
Over the past few centuries, human activity has wrought dramatic changes in the natural systems that support human life. Planetary health is a useful concept for health profession education (HPE) teaching and practice because it situates health within a broader understanding of the interdependent socio-ecological drivers of human and planetary health. It facilitates novel ways of protecting both population health and the natural environment on which human health and well-being depends. This paper focuses on the climate crisis as an example of the relationship between environmental change, healthcare, and education. We analyze how HPE can help decarbonize the healthcare sector to address both climate change and inequity in health outcomes. Based on the healthcare practitioner's mandate of beneficence, we propose simple learning objectives to equip HPE graduates with the knowledge, skills, and values to create a sustainable health system, using carbon emission reductions as an example. These learning objectives can be integrated into HPE without adding unduly to the curriculum load.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".