Resilience mediates the association between alexithymia and stress in Chinese medical students during the COVID‐19 pandemic
Bibliographic record
Abstract
Background: Evidence indicates that medical students have had high rates of mental health problems, especially during the COVID-19 pandemic, which could be affected by alexithymia-a marked dysfunction in emotional awareness, social attachment and interpersonal relationships-and stress. However, psychological resilience might relieve alexithymia and stress levels. Aims: This study aimed to investigate the role of resilience in alexithymia and stress in medical students. Methods: A total of 470 medical students completed online and offline surveys, including the Toronto Alexithymia Scale-20 (TAS-20), the Connor-Davidson Resilience Scale (CD-RISC) and the College Student Stress Questionnaire (CSSQ). The data of five participants were excluded because of a lack of integrity. Mann-Whitney U test or Kruskal-Wallis test was used to compare group differences in the CD-RISC scores among categorical variables. Spearman correlation analysis was employed to evaluate the associations between resilience and alexithymia and between resilience and stress. Mediation analysis was used to test the mediating effect of resilience between alexithymia and stress. Results: Of the medical students considered in the analysis, 382 (81.28%) were female and 88 (18.72%) were male. There was a significant negative correlation between the TAS-20 scores and the total and subtotal CD-RISC scores (p<0.001). The CSSQ scores also significantly negatively correlated with the total and subtotal CD-RISC scores (p<0.001). Resilience mediated the relationship between alexithymia and stress (total effect=1.044 7, p<0.001). The indirect effect of alexithymia significantly impacted stress through resilience (effect=0.167 0, 95% CI: 0.069 to 0.281). Conclusions: Our findings suggest that resilience might effectively reduce alexithymia and stress. They also contributed to a better understanding of the mediating effects of resilience on alexithymia and stress during the COVID-19 pandemic. The evidence from these results encourages universities to focus on improving students' resilience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".