Prediction of Mental Health based on Emotional Alexithymia and Marital Burnout of Women Affected by Infidelity
Bibliographic record
Abstract
Aim: The purpose of this study was to predict mental health based on emotional alexithymia and marital burnout of women affected by infidelity. Method: The current research was descriptive of predictive correlation type. The statistical population of the present study included women who referred to the counseling centers of District 5 of Tehran with a history of marital infidelity in 2022. According to the conducted research, 150 women visited the counseling centers of District 5 of Tehran in a period of 3 months, and 108 women were considered as a statistical sample through random sampling and Morgan's table. The research tools were Goldberg mental health questionnaires (1972), Toronto Alexithymia Scale (1994) and Pines marital burnout (2002). Kolmogorov-Smirnov test, Pearson correlation coefficient and multiple regression were used to analyze the data. Results: The results showed that the correlation coefficient between mental health and alexithymia is (0.380) and between mental health and marital burnout (0.568), which shows that there is a correlation between mental health and alexithymia and marital burnout of women affected by infidelity at the error level of 0.1. 0 and with 99 percent confidence, there is a significant positive and direct relationship. Also, regression analysis showed that emotional alexithymia and marital burnout have an effect on the mental health of women affected by infidelity (p<0.05), thus with 95% confidence, the contribution of emotional alexithymia is 28% and marital burnout is 44% on the mental health of women affected by betrayal. Conclusion: Marital infidelity has a great contribution to the level of emotional alexithymia and marital boredom, and in turn, this variable has a great impact on the mental health of couples, and by teaching couples how to achieve intimacy skills, steps can be taken to reduce marital infidelity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".