Psychosocial support during displacement due to a natural disaster: relationships with distress in a lower-middle income country
Bibliographic record
Abstract
BACKGROUND: Past studies show relationships between disaster-related displacement and adverse psychosocial health outcomes. The development of psychosocial interventions following displacement is thus increasingly prioritized. However, data from low- and middle-income countries (LMICs) are lacking. In October 2017, the population of Ambae Island in Vanuatu, a lower-middle income country, was temporarily displaced due to volcanic activity. We analyzed distress among adults displaced due to the event and differences based on the psychosocial support they received. METHODS: Data on experiences during displacement, distress and psychosocial support were collected from 443 adults 2-3 wk after repatriation to Ambae Island. Four support categories were identified: Healthcare professional, Traditional/community, Not available and Not wanted. We analyzed differences in distress by sex and group using one-way ANOVA and generalized linear models. RESULTS: Mean distress scores were higher among women (1.90, SD=0.97) than men (1.64, SD=0.98) (p<0.004). In multivariate models, psychosocial support group was associated with distress among women (p=0.033), with higher scores among women who reported no available support compared with every other group. Both healthcare professional and traditional support networks were widely used. CONCLUSIONS: Women might be particularly vulnerable to distress during disaster-related displacement in LMICs, and those who report a lack of support might be at greater risk. Both healthcare professional and traditional networks provide important sources of support that are widely used and might help to ameliorate symptoms.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".