Strengthening Adaptation Planning and Action to Climate-Related Health Impacts in Pacific Islands Countries: Tonga
Bibliographic record
Abstract
A natural hazard and climate change vulnerability and adaptation tool was applied in Tonga to identify health and health system-related climate and natural hazards, and to create and prioritize adaptation strategies and opportunities. During a 2-day multi-sectoral workshop, expert stakeholders prioritized the most extreme health-related and health system risks and devised a series of adaptation strategies. A series of health and health system impacts were identified and related to: cyclones/severe storms, increased average and extreme temperatures, flooding (including landslides), drought, wildfire, tsunami, earthquakes and volcanic activity. The main adaptation strategies identified improving drinking water security; development of Government procedures for drought management; linking health and climate data; increasing food security; improvements in urban design; training health workers; increasing evacuation center resilience in villages; increased research into management responses and enforcing and updating the building code. Adaptation to the health and health system impacts explored during the workshop include many outside the scope of the health system. This paper highlights the importance of multi-stakeholder engagement and co-planning to anticipate and plan for natural hazard and climate-related health and health system impacts and; benefits of establishing and using expert knowledge to determine health adaptation connections, build bridges across sectors and prioritize strategies in the absence of climate and health attributable information.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".