General Practice
Bibliographic record
Abstract
This chapter looks at the shared sustainability patterns between different systems of primary medical care in a number of high-income countries. The legal and local frameworks in each country mean that different priorities for sustainability have emerged. Generally, 4%–5% (or more) of these countries’ carbon footprint is from health care, and primary health care is about 25% of this. Prescribing medication (especially inhalers) is 60% of the primary care footprint generally. The inverse care law is closely linked to environmental sustainability, as marginalised groups suffer in both ways. There is progress on procurement and metered dose inhalers, but a general lack of urgency compared to COVID. Prevention is discussed as the best route to more sustainable care, suggesting a need for real conversations about diets and transport. As a result of planetary health changes, people’s health issues are changing, and examples of this are shared (air pollution is addressed more in another chapter). This leads to the suggestion that primary care’s voice should be clear that environmental issues are health issues. Detailed general practitioner (GP) perspectives and initiatives from Australia, Canada, England, Ireland, Northern Ireland, Scotland, and Wales are shared. This review of the challenges and how different networks and countries have found ways forwards offers inspiration and learning.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.505 | 0.304 |
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".