Patterns of distress and psychosocial support 2 years post-displacement following a natural disaster in a lower middle income country
Bibliographic record
Abstract
Background Displacement due to natural disaster exposure is a major source of distress, and disproportionately affects people in low- and middle-income countries (LMICs). Public mental health resources following natural disasters and displacement are often limited in LMICs. In 2017, the population of one island in Vanuatu, a lower-middle income country, was displaced due to volcanic activity. Following the launch of a public mental health policy in 2009, psychosocial support interventions are increasingly available, providing an opportunity to assess relationships with distress following displacement. Methods 440 people contributed data. We assessed distress using a local adaptation of the Impact of Event Scale-Revised, and types of psychosocial support available and received, including from health professionals, support groups, and traditional networks such as chiefs, traditional healers, and church leaders. We analyzed relationships between distress and psychosocial support, controlling for sociodemographic covariates. Results Professional and group support was reported available by 86.8–95.1% of participants. Traditional support networks were widely used, especially by men. Availability of professional support predicted lower distress among men (p < 0.001) and women (p = 0.015) ( ηp2 = 0.026–0.083). Consulting church leaders for psychosocial support was associated with higher distress among men (p = 0.026) and women (p = 0.023) ( ηp2 = 0.024–0.031). Use of professional and group support was lower than reported availability. Discussion Increased collaboration between professional and traditional support networks could help respond to mental health needs following natural disasters in LMICs with limited infrastructure. Providing training and resources to church leaders might be a specific target for improvement. Promoting use of available services represents a continued public health need.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".