Psychosocial Needs Assessment among Earthquake Survivors in Lorestan Province with an Emphasis on the Vulnerable Groups
Bibliographic record
Abstract
INTRODUCTION: Iran is one of the ten most earthquake prone countries in the world. Earthquakes not only cause new psychological needs among the population but particularly so when one considers vulnerable groups. This in - depth study was conducted with the aim of assessing psychosocial needs six months after an earthquake happened in the west of the county in Lorestan province. METHODS: This is a qualitative study using focus group discussion that focuses mainly on the vulnerable groups (women, children, elderly and disabled people) after an earthquake in Bozazna; a village in Lorestan province in western part of Iran. FINDINGS: Results of the psychosocial assessment indicated feelings of anxiety and worries in four vulnerable groups. Horror, hyper-excitement, avoidance and disturbing thoughts were observed in all groups with the exception of the elderly. Educational failures, loneliness and isolation were highlighted in children. All groups encountered socio-economic needs that included loss of assets and sense of insecurity and also reproductive problems were reported in women's group. DISCUSSION AND CONCLUSION: Modification of a protocol on psychosocial support considering the context of the rural and urban areas with emphasis on the specific needs of the vulnerable groups is an appropriate strategy in crisis management. It seems that appropriate public awareness regarding assistance programs can be effective in reducing stress and needs of disaster survivors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".