Education for sustainable health care: From learning to professional practice
Bibliographic record
Abstract
A number of planetary boundaries, including climate change as a result of greenhouse gas emissions, has already been exceeded. This situation has deleterious consequences for public health. Paradoxically, 4.4% of these emissions are attributable to the healthcare sector. These problems have not been sufficiently acknowledged in health professions curricula. This paper addresses two main issues, humanistic learning and the application of knowledge acquisition to clinical practice. Humanistic learning principles can be used to emphasize learner-centered approaches, including knowledge acquisition and reflection to increase self-awareness. Applying humanistic principles in everyday life and clinical practice can encourage stewardship, assisting students to become agents for change. In terms of knowledge and skills application to clinical practice, an overview of varied and novel approaches of how sustainable education can be integrated at different stages of training across several health care professions is provided. The Health and Environment Adaptive Response Taskforce (HEART) platform as an example of creating empowered learners, the NurSusTOOLKIT, a multi-disciplinary collaboration offering free adaptable educational resources for educators and the Greener Anaesthesia and Sustainability Project (GASP), an example of bridging the transition to clinical practice, are described.
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How this classification was reachedexpand
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".