Women’s Mental Health as the Basis of Preventive Planning for Disasters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it