A qualitative study of what motivates, facilitates, and hinders climate-engaged healthcare trainees to advance healthcare sustainability
Bibliographic record
Abstract
Introduction: There is a critical need for low-carbon, environmentally-sustainable health systems in the climate crisis. Healthcare trainees can play a vital role in this process, and we have aimed to explore how they can be motivated and supported by faculties of medicine and health systems to pursue this ambition by conducting an exploratory, qualitative descriptive study of Canadian healthcare trainees engaged in healthcare sustainability initiatives. Materials and methods: Transcripts from individual in-depth interviews were analyzed to identify themes related to the actions that healthcare trainees can take to promote sustainable healthcare, as well as the motivators, barriers and facilitators of healthcare trainee engagement in sustainable healthcare. Results: = 17) engaged in a spectrum of healthcare sustainability initiatives, including education, quality improvement and advocacy. They were motivated to advance healthcare sustainability through positive role models, the health impacts of climate change, observation of unsustainable healthcare practices, and a sense of social responsibility. Participants articulated that supportive networks, access to resources and funding, and having a growth mindset were facilitators to their engagement. In contrast, the lack of institutional prioritization of healthcare sustainability, limitations of the trainee role, challenges finding allies, and the perceived futility of their individual actions were characterized as barriers. Discussion: Healthcare trainees could support healthcare decarbonization efforts if they are adequately supported by their learning environments. The study's findings can guide educational innovations and health systems transformations to motivate and empower healthcare trainees to reduce the climate impact of healthcare throughout their careers.
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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 | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".