ارزیابی ارتباط بین ناگویی هیجانی و مشکلات بین شخصی دانشجویان دانشگاه علوم پزشکی کرمانشاه
Bibliographic record
Abstract
Abstract: Background : The aim of this study was to determine relationship between alexithymia and interpersonal problems in Kermanshah University of medical sciences. Methods: The design of research was descriptive-analytic correlation. The statistical population was the entire male and female students of Kermanshah University of medical sciences include 3470 persons. Use of Morgan table, 400 people was selected as statistical sample by stratified random sampling. . All participants were asked to complete Farsi version of the Toronto Alexithymia Scale (FTAS-20), and Inventory of interpersonal Problems (IIP-60). The data were analyzed by the use of descriptive statistics and Pearson–correlation coefficient and independent t. test. Results: Analysis of the data revealed a significant positive correlation between alexithymia with interpersonal problems(r=0.14, P=0.004). Also, Finding showed that there was positive significant correlation between alexithymia and assertiveness(r=0.19, P=0.001), sincerity (P=0.001, r=0.20), responsibility(r=0.25, P=0.001), controlling (P=0.02, r=0.11). But other components (openness and compliancy), were not significant. There is a significant difference in alexithymia between men (40.36) and women (37.35), but this difference was not significant for interpersonal problems. Conclusion: Based on finding of present study, alexithymia was correlated positively with interpersonal problems, so presenting educational planning can regulate emotion and prevent students' interpersonal problems. Key words: Alexithymia, university of medical sciences, interpersonal problems.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.010 |
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".