Building an environmentally accountable medical curriculum through international collaboration
Bibliographic record
Abstract
BACKGROUND: Global environmental change is associated with significant health threats. The medical profession can address this challenge through advocacy, health system adaptation and workforce preparedness. Stewardship of health systems with attention to their environmental impacts can contribute to mitigation of and adaptation to negative health impacts of environmental change. Medical schools have an integral role in training doctors who understand the interdependence of ecosystems and human health. Yet integrating environmental perspectives into busy medical curricula is not a simple task. CONTENT: At the 2016 Association for Medical Education in Europe conference, medical educators, students and clinicians from six continents discussed these challenges in a participatory workshop. Here we reflect on emerging themes from the workshop and how to plan for curricular change. Firstly, we outline recent developments in environmental health and associated medical education. Secondly, we reflect on our process and outcomes during this innovative approach to international collaboration. Thirdly, we present learning objectives which cover core content for environmentally accountable medical curricula, developed through a reflective process of international collaboration integrating current literature and the workshop outcomes. CONCLUSIONS: International collaboration can bring together diverse perspectives and provide critical insights for the inclusion of environmental health into basic education for medical practitioners.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.030 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".