Social determinants of mental health of women living in slum: a systematic review
Bibliographic record
Abstract
OBJECTIVE: With the rise of urbanization globally, the problem of living in slums has become a problem for the civil society. As a vulnerable segment, women make up half of the population in these regions; therefore, women's mental health has always been a concern. The purpose of this study was to review the social determinants of mental health in women living in slum areas. METHODS: We systematically reviewed articles published between 2009 and 2019 on the social determinants of women's mental health in SID, Magiran, Google scholar, PubMed, Scopus, Science Direct, Embase, MEDLINE, PsychINFO, and PsychARTICLES databases using MeSH keywords according to PRISMA guidelines. The quality of the studies was assessed depending on the type of study using Ottawa Newcastle" scale and Joanna Briggs Institute quality assessment tools. Finally, 23 studies were analyzed. RESULTS: Different social determinants influenced the mental health of women living in slum areas. Among the structural determinants, the socioeconomic level had the highest frequency, and gender was in the second rank with the highest correlation with poorer women's mental health status. Among the intermediate determinants, living conditions, food insecurity, social capital, and social support were most frequently associated with mental health status. CONCLUSION: Women living in slum areas are prone to developing mental disorders and poorer mental health; therefore, supporting these women and creating job opportunities to raise their incomes and, subsequently, improve their social, economic, and living conditions should be taken into consideration. In addition, this requires careful planning and comprehensive social support.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".