Nurses and stress: recognizing causes and seeking solutions
Bibliographic record
Abstract
AIMS: To identify, from the perspectives of nurses, occupational stressors and ways in which they may be reduced. BACKGROUND: Nurses commonly experience high levels of occupational stress, with negative consequences for their physical and psychological health, health-care organisations and community. There is minimal research on reducing occupational stress. METHOD: Six focus groups were conducted with 38 registered nurses using a qualitative exploratory approach. Participants were asked to identify sources of occupational stress and possible workplace initiatives to reduce stress. FINDINGS: Sources of occupational stress were: high workloads, unavailability of doctors, unsupportive management, human resource issues, interpersonal issues, patients' relatives, shift work, car parking, handover procedures, no common area for nurses, not progressing at work and patient mental health. Suggestions for reduction included: workload modification, non-ward-based initiatives, changing shift hours, forwarding suggestions for change, music, special events, organisational development, ensuring nurses get breaks, massage therapists, acknowledgement from management and leadership within wards. CONCLUSION: The findings highlight the need to understand local perspectives and the importance of involving nurses in identifying initiatives to reduce occupational stress. IMPLICATIONS FOR NURSING MANAGEMENT: Health-care environments can be enhanced through local understanding of the occupational stressors and productively engaging nurses in developing stress reduction initiatives. Nurse managers must facilitate such processes.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".