Climate Change and Mental Health in the Philippines: a systematic review
Bibliographic record
Abstract
The Philippines are at the forefront of climate change impacts, including those related to health and well-being, but information on mental health and well-being are typically underreported. To help address this research lacuna, we conducted a systematic literature review. We aimed to provide an overview of current research knowledge and research gaps regarding the impacts of climate change outcomes on Filipinos’ mental health and well-being. Consulting 8 databases, we identified 951 records. The final analysis included 32 studies: 16 quantitative, 11 qualitative, 2 longitudinal, 2 experimental, and 1 published report. A narrative synthesis has been performed to synthesize the findings from included studies. Studies were presented in four sections: 1) Risks to mental health following a natural disaster, 2) Determinants of post-traumatic stress disorder risks, 3) Resilience and post-traumatic growth following natural disasters, and 4) Personal experiences and other mental health outcomes. Reviewed data show that climate change outcomes strongly and negatively impact Filippino’s mental health and well-being. Climate change outcomes also, negatively affect mental health through indirect (e.g., sleep disorders) and long-term pathways for example by being exposed to stressors such as migration, conflict, and violence. A set of coping strategies was identified which include banding together, mobilizing health experts, and expanding the local relationships with health workers. Future prospective studies should assess the effects of rising sea levels and vector-borne diseases among frontline communities. More interventional studies assessing preventive interventions and health promotion initiatives should be carried out to mitigate mental health disorders and improve well-being, thus contributing to improved health outcomes.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".