Global primary care as an incubator for good ethical practice
Bibliographic record
Abstract
The global COVID-19 pandemic has exposed the fragility of national and international healthcare organisations, and tested the morality and integrity of health care across the world.On the frontline of care, physicians and healthcare workers were exposed to risks and poorly protected, with burnout rates high, moral injury substantial, and many contemplating leaving the profession. 1 Would reflection on the ethics of family medicine provide a means of re-engaging with the meaning of being a family doctor?Does this have wider implications for ethics education around the world?Inspired by the joint conference of the World Organization of Family Doctors (WONCA) and the Royal College of General Practitioners in London in 2022, we consider these questions and invite a global cross-disciplinary dialogue.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.023 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.041 | 0.044 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".