Is Divorce Predictable among Iranian Couples?
Bibliographic record
Abstract
Divorce is considered as an important social and public health concern worldwide. The aim of this study was to identify divorce’s social and economic contributors among Iranian couples. This case-control study was conducted on 60 divorced and their neighboring 64 still-married couples with approximately the same date of marriage. The required information was obtained from consultant administrated forms which are used routinely by Iranian family consulting centers. An interview-administered questionnaire with almost the same structure and questions was used to obtain information from still-married couples. Based on the results of multivariable analysis and (stepwise) selection of the study variables, significant associations between divorce and employment of both husbands and wives, education of husband, and the couple’s accommodation statuses were found. Accordingly, wife's (OR unemployed/self-employed=4.97, 95%CI: 1.38-21.61, P=0.001) and husband's (OR unemployed/self-employed =17.45, 95%CI: 3.56-123.98, P=0.001) unemployment, less educated husband's (OR primary or secondary/higher education =23.98, 95%CI=4.04-237.05, p=0.001) and couples with shared accommodation (OR dependent/independent= 5.99, 95%CI=2.54-17.72, P<0.001) were at higher risk of divorce. ROC analysis suggested that divorce can be confidently predicted by the above factors (AUC=0.882 95%CI: 0.816-0.948) with 66.7% sensitivity and 92.6% specificity. This study introduced several predictors, which can be used by family consultants and psychologists to recognize high risk marrying or married couples to prevent divorce and to help couples to obtain and sustain healthier marriages and stronger family relationships.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".