A peer-led group intervention based on relaxation (soRELAX) to improve well-being and mental health in nursing students: A mixed method pilot study
Bibliographic record
Abstract
BACKGROUND: The well-being of nursing students is strongly affected by their mental health. PURPOSE: The aim was to evaluate the feasibility, acceptability, and preliminary effects of a peer-led support group intervention based on autogenic training (soRELAX) on the well-being and mental health of nursing students. METHODS: A pilot mixed convergent design was used with a single group and three assessments: baseline, at 7 weeks, and at 12 weeks. The intervention was delivered online by 15 trained peers in small groups over 7 weeks. Recruitment, attrition, and completion rates were calculated. Well-being, stress, distress, anxiety, depression, social support, mindfulness, and performance were measured. Participants' perceptions were collected in online semistructured interviews. RESULTS: Three-quarters of the 55 nursing students completed at least six sessions. Results showed a significant increase in well-being and mindfulness and a significant decrease in stress, distress, anxiety, and depression symptoms at 7 and 12 weeks. Participants said that they felt more self-aware and more aware of what was causing them stress. CONCLUSIONS: soRELAX is a relatively feasible and acceptable intervention. Nursing students' well-being, mental health, and mindfulness were significantly improved after the intervention. This improvement was maintained at three months.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".