Prediction of Marital Burnout Based on Automatic Negative Thoughts and Alexithymia among Couples
Bibliographic record
Abstract
Background & aim: Lack of objective expression of emotions leads to the experience of unpleasant thoughts and evocations, followed by the non-recognition of one’s emotions and feelings. This is identified as one of the most important factors accounting for marital conflicts. The purpose of this study was to predict marital burnout based on negative automatic thoughts and alexithymia among couples in Shiraz, Iran. Methods: This correlational study was conducted on 150 couples referring to four counseling centers located in districts 1 and 2 of Shiraz in 2018-2019. The study population was selected using a multistage cluster sampling method. The data were collected using the burnout measure developed by Pines, automatic negative thoughts questionnaire, and twenty-item Toronto alexithymia scale on a self-report basis. Data analysis was performed in SPSS software (version 21) using regression analysis and Pearson correlation coefficient at a significance level of ≤ 0.05. Results: The results showed that the dimensions of an automatic negative thoughts could predict marital burnout positively and significantly (P≤0.05). The coefficient was obtained as 0.27, meaning that automatic negative thoughts, alexithymia, age, and education could predict marital burnout at 27%. Conclusion: Alexithymia and automatic negative thoughts could eventually lead to the elevation of dissatisfaction with marriage due to exerting a negative effect on maintaining a strong emotional relationship between partners.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".