Examining the Relationship Between Early Maladaptive Schemas and Alexithymia with Emotional Divorce Among Married Female Students
Bibliographic record
Abstract
Objective: This study aimed to investigate the relationship between early maladaptive schemas (EMS), alexithymia, and emotional divorce among married female students, focusing on their predictive roles in marital detachment. Methods: A correlational design was employed with 240 married female students recruited via convenience sampling from Islamic Azad University, Tonekabon Branch (2023–2024 academic year). Participants completed validated self-report measures: the Young Schema Questionnaire-Short Form (YSQ-SF; Young, 1995) assessing EMS across five domains (e.g., Impaired Limits, Other-Directedness), the Toronto Alexithymia Scale (TAS-20; Bagby et al., 1994) measuring alexithymia subscales (Difficulty Identifying Feelings, Difficulty Describing Feelings, Externally Oriented Thinking), and the Gottman Emotional Divorce Questionnaire (GEDQ; Gottman, 1997). Data were analyzed using Pearson’s correlation and stepwise regression via SPSS-26, with significance set at *p* < .01. Findings: Significant correlations emerged between EMS domains, alexithymia, and emotional divorce (*r* = 0.18–0.53, *p* < .01). Stepwise regression revealed Impaired Limits as the strongest predictor (β = 0.39, *p* < .01), explaining 15% of variance, followed by incremental contributions from Difficulty Identifying Feelings (ΔR² = 3%, β = 0.23), Other-Directedness (ΔR² = 1%, β = 0.13), Externally Oriented Thinking (ΔR² = 2%, β = 0.18), and Impaired Autonomy (ΔR² = 2%, β = -0.22), cumulatively accounting for 23% of variance (F = 15.16, *p* < .01). Negative β for Impaired Autonomy suggested suppression effects. Conclusion: EMS and alexithymia significantly predict emotional divorce, with Impaired Limits and emotion-regulation deficits being central drivers. Findings underscore the need for schema-focused interventions (e.g., schema therapy) and emotion-regulation training in marital counseling to mitigate detachment. Future research should explore dyadic interactions and cultural moderators to refine predictive models.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".