Faculty Development for Education for Sustainable Health Care: A University System-Wide Initiative to Transform Health Professional Education
Bibliographic record
Abstract
Health professionals (HPs) are increasingly called upon to care for patients experiencing the health impacts of climate change, while working in the high eco-footprint health care system, which is starting to embrace a culture of sustainability. HPs are uniquely positioned to drive health care culture toward ecological responsibility and, consequently, improve patient care, health equity, and public health. Education for sustainable health care (ESHC or ESH) is the first step in developing health care practitioners able to think critically about and act upon the health impacts of the climate crisis. University of California Education for Sustainable Healthcare (UC-ESH) Faculty Development Initiative was developed to address the following goals: educate faculty on eco-medical literacy, empower faculty to build community and lead ESH at their institutions, and expand coverage of ESH to reach students beyond those for whom sustainability is already a focus. The initiative provided training to faculty across health professions and 6 health science campuses to integrate ESH into their courses using the train-the-trainer model, key knowledge and pedagogical skills, and longitudinal guidance and networking opportunities. Using a survey, questionnaire, and interviews, the initiative was evaluated using the process/elements and product/outcomes steps of the Context, Input, Process, and Product evaluation model. The UC-ESH educated over 100 faculty members and led to ESH integration into 99 existing and new courses that subsequently reached over 7,000 learners. The UC-ESH increased empowerment, awareness, and knowledge about the climate crisis, and built an ESH community of practice. Initiative elements that contributed to these outcomes included engaging training; creation of supportive group dynamics; helpful resources and activities; ongoing support; and integration approaches to ESH. This university-system-wide initiative provides a transferable model to institutions, schools, and departments seeking to develop eco-medical literate faculty who educate their students about the climate, ecosystem, and health crisis.
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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 | 0.016 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".