Understanding national barriers to climate change adaptation for public health - a global survey
Bibliographic record
Abstract
Abstract Background Climate change has introduced a series of unprecedented threats to human health, ranging from rising food and water insecurity to deteriorating air quality, novel disease outbreaks, and intensifying natural disasters. The Paris Agreement pushes countries to develop adaptation plans that will protect human health from the worst impacts of climate change -a process referred to as climate change adaptation (CCA). Yet despite international pressure and escalating health threats, vast shortcomings persist in national CCA for public health progress. Thus, we investigated the major governance constraints underlying these trends. Methods A mixed-methods online survey was distributed to representatives of national public health associations and societies of 82 member countries under the World Federation of Public Health Associations. Results 9 of the 11 respondent countries (82%) affirmed the existence of a national CCA plan that includes an explicit public health focus. All respondees listed governance challenges in developing and operationalising their national CCA agenda. The major identified barriers to CCA for public health progress were lack of inter-government policy coordination and insufficient political will to mobilize human and non-human resources in support of public health-oriented adaptation efforts. Conclusions Climate change-driven amplification of global health risks necessitates that all nations generate clear CCA plans to protect human health. Our findings assist by highlighting the need for new platforms for organizational collaboration/networked governance and enhanced forums for CCA agenda-setting and ambition-raising. Such forms of enriched knowledge may facilitate decision-making amongst key public health stakeholders and global institutions for how best to align climate advocacy and country-wide support initiatives with cross-cutting national needs and constraints. Key messages Climate change-driven amplification of global health risks necessitates that all nations generate clear climate change adaptation plans to protect human health. New platforms for organizational collaboration/networked governance and enhanced forums for adaptation agenda-setting and ambition-raising may significantly bolster public health adaptation progress.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".