structural equation modeling of personality traits and marital conflicts with the mediating role of alexithymia
Bibliographic record
Abstract
The aim of this study was to determine the appropriateness of structural equation modeling of personality traits and marital conflicts with the mediating role of alexithymia. this research is applied and in terms of methodology, is the descriptive of correlation between structural equations. The statistical population of this study included all married people who referred to the psychological clinics of Tehran's District 2 in the period from February 2016 to July 2017, which is at least one year after their married life. In order to sample 300 people from the mentioned community, they were randomly selected by voluntary sampling method. Data collection questionnaires marital conflict questionnaire (MCQ), personality traits inventory (NEO), and Toronto's Emotional Disappointment (TAS_20) were used to collect data. The data were collected using statistical method of structural equation modeling (SEM) using AMOS_22 software. The results showed that the fit indices have desirable values and the data of this study have a good fit with the research model and also personality traits are related to the mediating role of alexithymia with marital conflicts. The conclusion is that personality traits can have a significant effect on alexithymia, which in turn can cause marital conflict and with the necessary training can reduce marital conflict as much as possible.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".