Prioritization of factors related to mental health of women during an earthquake: A qualitative study
Bibliographic record
Abstract
BACKGROUND: According to the fact that women account for the highest rate of victims of mental health loss during disasters, prioritization of their requirements is of remarkable significance. Therefore, the present study was carried out with the aim to prioritize factors related to women's mental health during an earthquake. METHODS: This study was conducted using the Delphi method in 2017. Participants consisted of experts including psychologists, counselors and psychiatrists, social medicine specialists, and psychiatric-mental health nurses with experience in service and disaster awareness, especially earthquake. They were selected using purposive and snowball sampling methods. The Delphi method was used in 2 rounds with 21 components and the final attendance of 19 participants and the collective agreement of 75%. RESULTS: Of the 21 factors related to women's mental health during the earthquake, the following components were, respectively, preferred by the participants: psychological well-being training, group relationships and support of women in emergency situations, fair treatment in the provision of rights and services to women, crisis awareness and management of behavior and difficult conditions, and social security during disasters with the total mean standard deviation (SD) of 13.4 (2.4), 13.0 (2.4), 12.7 (2.5), 12.5 (2.4), and 12.3 (2.2), respectively. CONCLUSION: Training in the field of psychology and disasters, and social and cultural factors were prioritized among factors related to women's mental health during an earthquake. Therefore, the results of this study can be applied by the planners and executives of mental health, women and disasters, and the women's community itself.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".