Mindfulness for mediating the relationship between self-control and alexithymia among Chinese medical students: A structural equation modeling analysis
Bibliographic record
Abstract
Backgrounds: Medical students are prone to experience alexithymia due to academic work overload, which could increase the prevalence of mental illness such as anxiety and depression. The purpose of our study was to estimate the levels of alexithymia and to explore the relationships between alexithymia, self-control, and mindfulness among medical students. Materials and methods: From March 18th, 2021 to April 9th, 2021, a cross-sectional study with stratified sampling was carried out in China Medical University, Liaoning Province, China. A total of 1,013 medical students participated in this study. The questionnaires pertaining to the Toronto Alexithymia Scale (TAS-26), the Five Facet Mindfulness Questionnaire (FFMQ), and the Self-control Scale (SCS) were used to assess the levels of alexithymia, mindfulness and self-control. We used Hierarchical Multiple Regression (HMR) and structural equation modeling to explore the mediating role of mindfulness between self-control and alexithymia. Results: < 0.01). Mindfulness mediated the relationship between self-control and alexithymia (a*b = -0.06, BCa 95% CI: -0.09 to -0.031, Percentile 95% CI: -0.089 to -0.031). Conclusion: Chinese medical students experienced high levels of alexithymia. Self-control could directly attenuate alexithymia for medical students and indirectly affect alexithymia through the mediating path of mindfulness. Initiatives for self-control ability enhancement should be provided to medical students to combat alexithymia. And interventions on mindfulness training should be developed to prevent from alexithymia and promote their mental health.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".