Mental health and psychosocial interventions in the context of climate change: a scoping review
Bibliographic record
Abstract
The evidence on the impacts of climate change on mental health and wellbeing is growing rapidly. The objective of this scoping review is to understand the extent and type of existing mental health and psychosocial interventions aimed at addressing the mental health and psychosocial impacts of climate change. A scoping review methodology was followed. MEDLINE, PsycINFO, and Web of Science databases were searched from inception to May 2022. Comprehensive gray literature search, including expert consultation, was conducted to identify interventions for which peer-reviewed academic literature may not yet be available. Data on intervention type, setting, climate stressor, mental health outcome, evaluation, and any other available details were extracted, and results were summarized narratively. Academic literature search identified 16 records and gray literature search identified a further 24 records. Altogether, 37 unique interventions or packages of interventions were identified. The interventions act at the levels of microsystem, mesosystem, exosystem, and macrosystem through diverse mechanisms. While most interventions have not been formally evaluated, promising preliminary results support interventions in low- and middle-income-country settings disproportionately affected by climate disasters. Interventions from multidisciplinary fields are emerging to reduce psychological distress and enhance mental health and wellbeing in the context of climate change. This scoping review details existing evidence on the interventions and summarizes intervention gaps and lessons learned to inform continued intervention development and scale-up interventions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".