Predicting Marital Conflicts Based on Emotional Ataxia with the Intermediary Variable of Couples' Psychological Toughness
Bibliographic record
Abstract
Aim: The purpose of the present study was to predict marital conflicts based on emotional ataxia with the intermediary variable of couples' psychological toughness. Method: The method of this research is descriptive and correlational. The statistical population of the research was all the couples in Shiraz city. In this research, the sample size according to the instructions of Bentler and Bonnet (1980) is 15 people for each variable, considering that the selected samples were 331 people, of which 31 people dropped out, and finally 300 people were selected through lottery using the sample method. were randomly selected. In this research, in order to collect information, Sanai Zaker Marital Conflicts Questionnaire (2007), Toronto Emotional Dyslexia Scale (1994) and Kobasa and Madi's Stubbornness Questionnaire (1982) were used. Pearson's correlation coefficient and stepwise regression analysis were used to analyze the data. Results: The results showed that there is a significant relationship between the score of marital conflict dimensions and emotional dyslexia (p=0.001); Also, there is a significant relationship between the score of dimensions of marital conflicts with the mediation of psychological toughness (p=0.001); The results of step-by-step regression analysis showed that marital conflicts can be predicted through the dimensions of emotional ataxia and the mediation of psychological toughness (p=0.001, β=0.46). Conclusion: The results of the present study indicate the importance of emotional intransigence and psychological toughness in predicting marital satisfaction, so it can be said that couples who have less psychological toughness experience higher marital conflicts.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".