The Prevalence and Predictors of Compassion Satisfaction, Burnout and Secondary Traumatic Stress in Registered Nurses in an Eastern Canadian Province: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: The quality of Registered Nurses' worklife is impacting nurses' mental health, and the standard of care received by clients. Contributing factors to nurses' stress are the trauma of continuous caring for those in great suffering, and adverse working conditions. OBJECTIVES: i) to explore the prevalence of work-related stress in a provincial sample of Registered Nurses; ii) to compare the levels of compassion satisfaction, burnout and secondary traumatic stress reported by nurses in hospital, community, non-direct care settings, and, iii) to identify factors that predict levels of nursing work stress. METHODS: A descriptive, predictive study with a self-report survey containing demographic questions and the Professional Quality of Life Scale was emailed to over 3,300 Registered Nurses. The scale measured the prevalence of three worklife indicators, compassion satisfaction, burnout and secondary traumatic stress. Multiple linear regression identified factors that predicted the levels of the three indicators. A subgroup analysis explored the quality of worklife based on three practice environments. FINDINGS: Nurses (n = 661) reported moderate compassion satisfaction, burnout, and secondary traumatic stress. The strongest predictor, satisfaction with one's current job, predicted high compassion satisfaction and lower burnout and secondary stress. The subgroup analysis identified hospital nurses as having the most work-related stress and the lowest level of compassion satisfaction. CONCLUSION: Innovative, collaborative action can transform nurses' practice environments. Organizational support is essential to bring about needed improvements.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".