General Practitioners’ responses to global climate change - lessons from clinical experience and the clinical method
Bibliographic record
Abstract
BACKGROUND: Climate change is a global public health problem that will require complex thinking if meaningful and effective solutions are to be achieved. In this conceptual paper we argue that GPs have much to bring to the issue of climate change from their wide-ranging clinical experience and from the principles underpinning their clinical methods. This experience and thinking calls forth particular contributions GPs can and should make to debate and action. DISCUSSION: We contend that the privileged experience and GP way of thinking can make valuable contributions when applied to climate change solutions. These include a lifetime of experience, reflection and epistemological application to first doing no harm, managing uncertainty, the ability to make necessary decisions while possessing incomplete information, an appreciation of complex adaptive systems, maintenance of homeostasis, vigilance for unintended consequences, and an appreciation of the importance of transdisciplinarity and interprofessionalism. SUMMARY: General practitioners have a long history of public health advocacy and in the case of climate change may bring a way of approaching complex human problems that could be applied to the dilemmas of climate change.
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 | 0.030 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.022 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".