Barriers and Facilitators to the Implementation of Mental Health and Psychosocial Support Programmes Following Natural Disasters in Developing Countries: a Systematic Review
Bibliographic record
Abstract
Climate change is leading to more frequent and intense natural disasters, with developing countries particularly at risk.However, most research concerning mental health and natural disasters is based in high-income country settings.It is critically important to provide a mental health response to such events, given the negative psychosocial impacts they elicit.The aim of this systematic review is to explore the barriers and facilitators to implementing mental health and psychosocial support (MHPSS) following natural disasters in developing countries.Eight databases were searched for relevant quantitative and qualitative studies from developing countries.Only studies reporting barriers and/or facilitators to delivering MHPSS in response to natural disasters in a low-or middle-income country were included and full texts were critically appraised using the McGill University Mixed Methods Appraisal Tool.Reported barriers and facilitators were extracted and analysed thematically.Thirty-seven studies were included in the review, reflecting a range of natural disaster settings and developing countries.Barriers to implementing MHPSS included cultural relevance, resources for mental health, accessibility, disaster specific factors and mental health stigma.Facilitators identified included social support, cultural relevance and task-sharing approaches.A number of practical approaches can be used to facilitate the implementation of MHPSS in developing country settings.However, more research is needed on MHPSS in the developing country natural disaster context, especially in Africa, and international policies and guidelines need to be re-evaluated using a decolonial lens. Impact statementThis paper explores barriers and facilitators to delivering mental health and psychosocial support in developing countries following natural disasters.Given the vulnerability of the Global South to climate change, including increasingly frequent and intense natural disasters, this paper offers important learnings for both policy and programme delivery to build climate resilience and facilitate disaster response.It also addresses a key gap in the literature, which to date has predominantly focused on high-income country settings or in humanitarian contexts in general rather than specifically natural disasters.
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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 | 0.020 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".