Family doctors to connect <i>global concerns</i> due to climate change with <i>local actions</i>: State‐of‐the art and some proposals
Bibliographic record
Abstract
Abstract Climate change (CC) is the most challenging environmental health (EH) concern. Air pollution is closely linked to CC. However, many CC‐health‐related conditions (i.e., allergic diseases, asthma, hypertension, fluid and electrolyte disorders, child and adult obesity, type 2 diabetes, vector‐borne diseases) are not usually counted, either because they do not cause death or require hospital admission/emergency triage. They are the vast majority of health care seeking generally treated by family doctors (FDs) and family pediatricians (FPs). FDs/FPs are often not aware of CC‐health‐impacts. Their potential role in tackling such a global challenge through their local influence on individual and collective attitudes and policies is not considered. Proper FD training could fill these gaps, raise awareness of their role, and implement EH FDs/FPs‐based surveillance networks to collect, analyze, interpret, and report EH data to inform EH‐related Policy. FDs and FPs, organized in sentinel physicians' networks, could play a key role in advising policymakers at the local and regional level in designing interventions adapted to climate‐related issues. Such experiences are rare worldwide and not well known. We will describe and discuss them in detail to share successful local examples.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".