Attitudes Toward Dating Violence, Social Impact, and Alexithymia in University Students: A Structural Equation Modeling
Bibliographic record
Abstract
This research was conducted to determine the effects of social impact and alexithymia on attitudes toward dating violence. Additionally, the interaction between them was examined. In this context, the effect of social impact on alexithymia was also examined. In addition, this research tried to determine whether alexithymia has a mediating role between social impact and attitudes toward dating violence. The study was descriptive and cross-sectional. There was a total of 582 participants in the study. The study used the Sociodemographic Questionnaire Form, Social Impact Scale (SIS), 20-Item Toronto Alexithymia Scale (TAS-20), and Dating Violence Scale (DVS) for data collection. The study used the descriptive statistics, spearman correlation analysis and structural equation modeling in the evaluation of the data. Participants’ SIS scores had a statistically significant and positive correlation with their TAS-20 scores (p < .01), while they had a statistically significant and negative correlation with their DVS scores (p < .01). In addition, a statistically significant and negative correlation was found between TAS-20 scores and DVS scores (p < .01). SIS scores directly affected the TAS-20 (effect value = 0.481; p = .001) and DVS scores (effect value = −0.405; p = .001). Similarly, the TAS-20 scores had a direct effect on the DVS scores (effect value = −0.261; p = .008). In addition to this direct effect of TAS-20 scores, there was a significant mediator effect between the SIS scores and DVS scores (effect value = −0.126; p = .008). In this study, it was established that social impact effects both alexithymia and attitudes toward dating violence, and moreover, alexithymia influences attitudes toward dating violence. Additionally, it was identified that alexithymia serves as a mediator in the relationship between social impact and attitudes toward dating violence.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".