Bibliographic record
Abstract
In Islamic culture, women are bound to accept more than one and up to four marriages of their husbands and this sharing may appear as a threat to them. The present study aimed to examine the jealousy, marital satisfaction, and mental health in a purposive sample of 192 women in polygamous marriages (first and second wives) with an age range of first wives 25-60 (M = 40.30, SD =7.86) and second wives 18-40 (M = 26.60, SD =7.41). The sample was drawn from Punjab. It was Correlational research with a cross-sectional design. Revised Dyadic Adjustment Scale[i]and Mental Health Inventory[ii] along with the newly developed indigenous Jealousy Scale for polygamous women[iii] were used as measures. Results revealed men's injustice and biased behavior toward their multiple wives cause jealousy and poor marital satisfaction in polygamous women which deteriorate their mental health. Findings showed that poor marital satisfaction mediated between jealousy and mental health (Psychological well-being and psychological distress). After controlling the poor marital satisfaction, jealousy significantly negatively predicted Mental Health (Psychological well-being) and positively predicted psychological distress but reduces the value which indicates partial mediation. The Implications of the findings were discussed for family settings, family therapists, and law advisors in the Pakistani socio-cultural context and wherever polygamy is allowed in the world. The study is also implemented to seek the original teachings and true picture of Islamic perspectives about polygamy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.953 | 0.967 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".