Empowerment of women and mental health improvement with a Preventive approach
Bibliographic record
Abstract
AIM AND BACKGROUND: Mental health and empowerment are two of the women's essential needs. These two related concepts play an important role in women's lives. Therefore, this study aimed to investigate empowerment of women and its relation with mental health problem prevention during difficult situations. METHODS: This qualitative study was conducted through semi-structured interviews with 33 experts in the fields of psychology, social sciences, women studies, medicine and crisis management specialists using snowball sampling in cities of Tehran, Isfahan, Tabriz, and Mashhad during the year 1395 (March 2016-March 2017). Samples were selected heterogeneously. The interview transcripts and codes were presented to the participants, and structural analysis was used for data evaluation. RESULTS: The factors related to empowerment of women with consideration to their mental health were determined based on Longew theory and interviews and include: welfare (primary needs (biological and security) and developmental needs (social needs and dignity), access (facilities and values), knowledge (about inequalities and rights), participation (in politics, decision-making and society), and control (implementation and institutionalization of the above-mentioned needs). CONCLUSIONS: The indicators determined in this study show that empowerment has an important role in determining women's real position in society. Since women make up half of the population and affect society as a whole, the advantages of empowerment of women will be felt in the entire society.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".