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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".