Adaptation and mitigation for the planetary health crisis: A scoping review from the perspective of primary health care providers
Bibliographic record
Abstract
Climate-related adverse health outcomes are on the rise worldwide, and primary health care providers are at the forefront of the growing climate-health crisis. There is an urgent need for a codification of solutions and strategies for adaptation, resilience, and transformation in primary health care. This scoping review sought to answer the following research question: "What strategies are being implemented across all forms of primary health care to adapt to and address the climate crisis?" After iterative axial coding of the 94 retained papers, 15 themes emerged: community engagement; reaching vulnerable populations; transdisciplinary and intersectoral collaboration; clinical strategies; research, surveillance; pluralism; patient education; continuing education and community of practice; benefits of nature; infrastructure resilience; advocacy; conservation; redefining health; provider wellbeing; and impact of health care. It behooves primary health care practitioners, especially those within dominant systems, to advocate strategies that promote health in all systems and policies. The planetary health crisis is a health crisis. It is urgent, it is human-created, and it can be mitigated.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".