Doctors' Efforts to Reform Medical Practice in England for Climate Change Mitigation: Insights for Implementing Medical Education and Training Reforms
Bibliographic record
Abstract
In this study, I explore how the ideas and experiences of doctors who have attempted to reform medical practice in England for climate change mitigation can enhance the implementation of similar reforms in medical education and training. I draw upon theories of climate justice, the social responsibility of medicine, critical reflective practice, and learning as a socio-cultural process to frame and investigate the research problem. I draw upon the critical-interpretive paradigm and the methodological recommendations of the phronetic social science framework to design the study. Applying these ideas, I conducted an interview study with 18 English doctors representing diverse clinical specialties, official designations, career stages, and social backgrounds. I documented how these doctors conceptualized reform problems and solutions, and attempted to enact change between 2006 and 2020. I also inquired into what enabling conditions and barriers they encountered in the process, and how they regard the consequences of their efforts, and envision future action. I then analyzed how their reflections and insights can improve the implementation of ongoing efforts to reform medical education and training in England to address climate change mitigation. My analysis shows that doctors’ efforts to reform professional practice can be adapted and applied to reform medical education and training in a variety of ways. Significant among these are: exercising/leveraging formal authority and leadership to drive top-down change, seeking new job roles to work on climate change mitigation, securing sustained sources of funds for reforms, building alliances in and outside the health system, creating networks for collaboration, generating new socio-environmentally informed ways of conceptualizing and practicing illness prevention and healthcare, and pursuing professional learning to develop additional capabilities to enact change. The scaling-up and wider application of reform initiatives such as these can have significant impacts in mitigating the threats and harms of climate change in England. They can inspire similar action in health systems in other global contexts as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".