Exploration of the Association between Nurses’ Moral Distress and Secondary Traumatic Stress Syndrome: Implications for Patient Safety in Mental Health Services
Bibliographic record
Abstract
Work-related moral distress (MD) and secondary traumatic stress syndrome (STSS) may be associated with compromised health status among health professionals, reduced productivity, and inadequate safety of care. We explored the association of MD with the severity of STSS symptoms, along with the mediating role of mental distress symptoms. Associations with emotional exhaustion and professional satisfaction were also assessed. This cross-sectional survey conducted in 206 mental health nurses (MHNs) was employed across public sector community and hospital settings in Cyprus. The analysis revealed that MD (measured by the modified Moral Distress Scale) was positively associated with both STSS (measured by the Secondary Traumatic Stress Scale) and mental distress symptoms (assessed by the General Health Questionnaire-28). The association of MD with STSS symptoms was partially mediated by mental distress symptoms. This association remained largely unchanged after adjusting for gender, age, education, rank, and intention to quit the job. Our findings provide preliminary evidence on the association between MD and STSS symptomatology in MHNs. Situations that may lead health professionals to be in moral distress seem to be mainly related to the work environment; thus interventions related to organizational empowerment of MHNs need to be developed.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".