Association between job strain, mental health and empathy among intensive care nurses
Bibliographic record
Abstract
BACKGROUND: Nursing shortage is a worldwide issue. It could influence factors such as job strain, nurses' mental health and nurse empathy towards patients. AIM: The aim of the study is to explore the associations between job strain, psychological distress, psychological well-being and empathy in intensive care units (ICUs). DESIGN: A cross-sectional descriptive correlational design. METHODS: Data were collected using questionnaires and an observation tool completed by three observers during a standardized clinical simulation (SCS). A total of 26 nurses practicing in three ICUs participated in the study, which took place over 3 days in December 2011. RESULTS: One dimension of job strain, psychological demand, was associated with two subscales of mental health (psychological distress positively and psychological well-being negatively). Positive correlations were demonstrated between psychological distress and nurse empathy as perceived by both the observers and the actor who played the role of patient. CONCLUSION: Some associations have been confirmed between job strain, psychological distress, psychological well-being and empathy in the ICU while others needs further investigation. RELEVANCE TO CLINICAL PRACTICE: It is important to reduce psychological demand among intensive care nurses in order to prevent psychological distress. The exploration of the connection between empathy and psychological distress should be advanced. This study suggests that SCSs provide an innovative approach that is useful for research.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".