2614 – Interventions to Reduce Stress in University Students: A Review and Meta-Analysis
Bibliographic record
Abstract
Background: Of late, highly publicized reports have revealed that approximately half of university students report moderate levels of stress-related mental health concerns including anxiety and depression. Other research reveals that university health services are only providing services to a small percentage of these students. Universities must therefore consider the use of accessible programs that address student stress and reduce resultant anxiety and depression. Method: A systematic review of the literature and meta-analysis was conducted to examine the effectiveness of interventions aimed at reducing stress in university students. Studies were eligible for inclusion if the assignment of study participants to experimental or control groups was by random allocation or parallel cohort design. Results: Twenty-four studies, involving 1431 students were included in the meta-analysis. The findings suggest that cognitive, behavioral and mindfulness-based interventions focused on stress reduction significantly reduce symptoms of anxiety. Further, despite variations in approaches in terms of length of intervention and specific components of the intervention (including such aspects as cognitive restructuring, relaxation and meditation) with students in different programs and in different countries, these results are remarkably consistent. Secondary outcomes included lower levels of depression and cortisol. Conclusion: This review and meta-analysis provides strong support that cognitive, behavioral and mindfulness-based approaches are effective in reducing the effects of stress on university students. Given the high rates of stress-related mental health problems reported by university students, universities are advised to examine means to provide opportunities for larger numbers of students to access these interventions.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.024 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".