Influence of family function on social anxiety among Chinese nursing students: The mediating role of alexithymia
Bibliographic record
Abstract
AIMS: This study aims to explore the relationship between family function, alexithymia and social anxiety among Chinese nursing students, especially to determine whether alexithymia acts as a mediator between family function and social anxiety among this social group. DESIGN: A cross-sectional study. METHODS: From January to March 2021, a cross-sectional study was conducted among 204 Chinese nursing students at a nursing school of a medical university in the Northeastern Region of China. The interaction anxiousness scale, APGAR family function scale and Tronto alexithymia scale were used for measurement of social anxiety, perceived family function and alexithymia respectively. The data were subjected to correlation analysis, multiple linear regression and structural equation modelling (SEM). RESULTS: Results indicated that social anxiety score was negatively correlated with perceived family function (r = -.232), but positively with alexithymia (r = .307). After controlling for demographic variables, family function and alexithymia could explain 14.5% of the total variance in social anxiety. The SEM results suggested that the effect of family function on social anxiety was partially mediated by alexithymia with a 36.9% mediating effect. CONCLUSIONS: This study reveals that alexithymia might partially mediate the impact of family function and social anxiety in Chinese nursing students. In this sense, improvement of alexithymia is expected to be an effective strategy to ameliorate the severity social anxiety in Chinese nursing students, especially for those with a dysfunctional family context.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".