Relaxation training methods for nurse managers in Hong Kong: a controlled study
Bibliographic record
Abstract
Nurse managers are under increased stress because of excessive workloads and hospitals' restructuring which is affecting their work tasks. High levels of stress could affect their mental health. Yet, few stress management training programmes are provided for this population. The purpose of this study was to apply stretch-release relaxation and cognitive relaxation training to enhance the mental health for nurse managers. A total of 65 nurse managers in Hong Kong were randomly assigned to stretch-release relaxation (n = 17), cognitive relaxation (n = 18), and a test control group (n = 35). Mental health status was assessed using the Chinese version of State-Trait Anxiety Inventory and the Chinese version of the General Health Questionnaire. Participants were assessed at the pretreatment session, the fourth posttreatment session, and at the 1-month follow-up session. The results revealed both the stretch-release and cognitive relaxation training enhanced mental health in nurse managers in Hong Kong. The application of relaxation training in enhancing mental health status for nurses and health professionals is discussed.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".