The relationship between alexithymia and shyness in nursing students with mediating roles of loneliness and social identity
Bibliographic record
Abstract
Background: Shyness is a main cause of poor results in the educational environment. The present study aimed at studying relationship between alexithymia (the inability to recognize or describe one’s own emotions) and shyness in nursing students, with mediating roles of loneliness and social identity. Methods: This descriptive-correlational study was based on structural equation modeling, specific regression equations, and a statistical population of 658 nursing students at Ahvaz University of Medical Sciences in 2018. A sample of 331 students was selected. To collect the data, Toronto’s Alexithymia Scale, Russell, Peplau and Cutrona’s Loneliness Scale, Safarinia’s Social Identity Questionnaire and Briggs, Cheek and Buss’ Shyness Scale were used. Results: The findings from regression analysis showed that there was a direct effect between alexithymia and shyness and an indirect effect between alexithymia and shyness with mediating factors of loneliness and social identity (P<0.01). In total, alexithymia, feelings of loneliness, and perception of social identity had a predictive power of 0.51% of shyness. Conclusion: The results of this study show the effect of alexithymia and the role of moderating influences on feelings of loneliness and social identity perceptions and shyness among nursing students, which can provide useful practical applications to advisers and trainers in order to improve the psychological state of nursing students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".