Women’s Mental Health as the Basis of Preventive Planning for Disasters
Bibliographic record
Abstract
Mental health affects all aspects of life, and its improvement is considered to be an effective strategy to achieve the human development indicators. This issue is of particular importance in women since they constitute half the population and play a pivotal role as family members and in survival in the periods of austerity. Enhancing mental health should be prioritized during crises and disasters when regular norms and order are replaced with chaos and disorder. With respect to public health, no individual is unaffected by such situations, while the changes are not similar in everyone. In this regard, studies suggest that women account for the majority of victims in disasters (1, 2), while other findings emphasize on the constructive role of women in families during crises (3). However, women have been less considered in health-related studies (4). Preventing mental problems in women would be possible by prioritizing them in preventive planning and educational and support programs (5). Given the importance of preventive policies in improving the mental health of the society (6), empowerment training should be implemented considering its direct association with mental health (7). One of the most effectual strategies for empowerment training involves changing the perceptions of the community toward women, which results in the enhancement of their mental health status. How to cite this article: Shooshtari Sh, Abedi MR, Bahrami M, Samouei R. Women’s Mental Health as the Basis of Preventive Planning for Disasters . J Saf Promot Inj Prev. 2017; 5(2):61 -62.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".