Alexithymia and negative emotions among nursing students: a moderated mediation model
Bibliographic record
Abstract
Nursing students, who comprise a high percentage of China's college students, experience many psychological problems; however, few studies explored the mechanisms underlying these problems. This cross-sectional study explored the relationships and mechanisms of depression, anxiety, stress, and narrative disorders in senior nursing students. Questionnaires were administered to 380 senior nursing students in Hubei Province using the Sociodemographic Questionnaire, Toronto Alexithymia-20 Scale, Perceived Social Support Scale, 10-Item Connor-Davidson Resilience Scale, and Depression-Anxiety-Stress Scale. After controlling for sociodemographic variables, Hayes' PROCESS macros were used to test how psychological resilience moderates the relationships among narrative disorders, negative affect, and perceived social support. Bootstrap confidence intervals tested for indirect effects. Correlation analyses revealed that alexithymia was correlated significantly positively with depression-anxiety-stress (r = 0.57, 0.56, and 0.58, resp.) and significantly negatively with perceived social support (r = 0-0.46). Psychological resilience was correlated significantly negatively with alexithymia (r=-0.39) and depression-anxiety-stress (r=-0.31, -0.30, and-0.32, resp.) but significantly positively with perceived social support(r = 0.50). Perceived social support was correlated significantly negatively with depression-anxiety-stress (r=-0.33, -0.34, and - 0.42 resp.). Stress was correlated significantly positively with anxiety and depression (r = 0.81 and 0.77, resp.). Psychological resilience was a partial mediator between depression and dysphoria (β=-0.08, p < 0.05). Dysphoria directly predicted anxiety (β = 0.31) and stress (β = 0.37); moreover,alexithymia predicted depression not only directly but also through the mediating effect of psychological resilience. Therefore, educators and clinical administrators must promote and recognise negative emotions among nursing students to help ensure the nursing workforce's stability.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".