Polygamy and poor mental health among Arab Bedouin women: do socioeconomic position and social support matter?
Bibliographic record
Abstract
OBJECTIVES: Polygamy is a complex phenomenon and a product of power relations, with deep cultural, social, economic, and political roots. Despite being banned in many countries, the practice persists and has been associated with women's marginalization and mental health sequelae. In this study, we sought to improve understanding of this ongoing, complex phenomenon by examining the contribution of socioeconomic position (SEP) and social support to the excess of depressive symptoms (DS) and poor self-rated health (SRH) among women in polygamous marriages compared to women in monogamous marriages. Measuring the contribution of these factors could facilitate policies and interventions aimed at protecting women's mental health. DESIGN: The study was conducted among a sample of Arab Bedouin women living in a marginalized community in southern Israel (N=464, age 18-50). The women were personally interviewed in 2008-2009. We then used logistic regression models to calculate the contribution of SEP (as defined by the women's education, family SEP, and household characteristics) and social support to excess of depressive symptoms and poor SRH among participants in polygamous versus monogamous marriages. RESULTS: About 23% of the participants were in polygamous marriages. These women reported almost twice the odds of depressive symptoms (OR=1.91, 95%CI=1.22, 2.99) and poorer SRH (OR=1.73, 95%CI=1.10, 2.72) than those in monogamous marriages. Women's education changed these associations slightly, but family SEP and household characteristics resulted in virtually no further change. Social support reduced the odds for poor SRH and DS by about 23% and 28%, respectively. CONCLUSION: Polygamy is associated with higher risk for poor mental health of women regardless of their SEP and education. Social support seems to have some protective effect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".