Predictors of Fear of Intimacy among University Students
Bibliographic record
Abstract
The study, which aims to examine the association between fear of intimacy, irrational thoughts and alexithymia in university students, was carried out in a correlational design based on a quantitative research approach. To collect the data, Fear-of-Intimacy Scale (FIS), Toronto Alexithymia Scale (TAS-20) and Irrational Beliefs Test (IBT) were used. The data were analysed with SPSS software. Pearson Product Moments Correlation Coefficient was used to examine the associations among fear of intimacy, irrational thoughts and alexithymia. The result of correlation analysis showed that there is a statistically significant association between fear of intimacy and alexithymia. Hierarchical regression analysis was applied to determine the original contribution of irrational thoughts and alexithymia in the explanation of fear of intimacy. In the first step, the predictive strength of irrational thoughts, need for approval, high expectations, tendency to blame and fear of intimacy were examined. The first regression model was significant and accounted for 6.8% of the total variance of fear of intimacy. It was determined that the tendency to blame and helplessness contributed significantly to the model. In the second step, alexithymia was added to the model with sub-dimensions. The second regression model was significant and accounted for 23.5% of the total variance of fear of intimacy. When the R2 change values were examined, the original contribution of the sub-dimensions of alexithymia to the total variance of fear of intimacy was 16.7%.
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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 | 0.000 | 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".