Effectiveness of a Mindfulness-Based Group Intervention for Chinese University Students with Sleep Problems
Bibliographic record
Abstract
The increasing prevalence of sleep disorders among university students should be taken seriously. Group counseling involving a mindfulness-based strategy may help prevent students from developing insomnia and subsequent mental health disorders. This study aimed to evaluate the ameliorating effects of a mindfulness-based group intervention on sleep problems and emotional symptoms in university students in China. Twenty-one university students (16 females, 22.71 ± 4.28 years) who were not on medication were recruited and assigned to the intervention group based on the criterion of high levels of sleep problems. Additionally, twenty-four university students (19 females, 24.50 ± 0.93 years) were included as a nonrandomized control group. Individuals in the intervention group participated in a two-hour group intervention once a week for eight sessions. All participants completed self-reported questionnaire baseline tests, postintervention tests, and one-month follow-ups on mindfulness, sleep quality, anxiety and depressive symptoms. Repeated-measures ANOVA was performed. The results revealed significant intervention effects, with significant differences observed between the two groups in mindfulness and sleep quality. However, there was no significant effect of the intervention on anxiety and depressive symptoms. This study contributes to a better understanding of the effectiveness of mindfulness-based intervention in addressing sleep problems in university 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".