Understanding national barriers to climate change adaptation for public health: a mixed-methods survey of national public health representatives
Bibliographic record
Abstract
Purpose To uncover the major government constraints to enactment and implementation of public health-targeted climate change adaptation (CCA) strategies in order to equip public health stakeholders and health advocates with the knowledge resources necessary to more effectively mobilize and support CCA for public health responses at the national level. Design/methodology/approach A mixed-methods online survey was distributed to the representatives of national public health associations and societies of 82 countries. The survey comprised 15 questions assessing national progress on CCA for public health and the effects of various institutional, economic/financial, technical and sociopolitical barriers on national adaptive capacity. Findings Survey responses from 11 countries indicated that national commitments to CCA for public health have increased markedly since prior assessments but significant shortcomings remain. The largest apparent barriers to progress in this domain were poor government coordination, lack of political will and inadequate adaptation finances. Originality/value This study is unique in relation to the prior literature on the topic in that it effectively captures an array of country-specific yet cross-cutting adaptation constraints across diverse national contexts. With a deepened understanding of the major determinants of national adaptive capacity, international actors can devise more effective, evidence-informed strategies to support national governments in responding to the health impacts of climate change.
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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 | 0.037 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".