Examining the Relationship Between Hospital Nurses' Structural Empowerment, Missed Nursing Care and Quality of Care: A Cross‐Sectional Study
Bibliographic record
Abstract
AIM: To examine relationships between structural empowerment, missed nursing care and quality of care among hospital-based, direct-care nurses. DESIGN: Cross-sectional study. METHODS: A convenience sample of 161 nurses completed the Conditions for Work Effectiveness-II Questionnaire, the MISSCARE and a single-question rating of quality of care. Correlation, T-tests, regression and ANOVA were used to analyse data. RESULTS: Nurses reported high structural empowerment (total CWEQ-II = 22.8). Higher empowerment was significantly correlated with less missed care. Most nurses (77.7%) worked at Magnet hospitals; however, no difference in missed care was found between Magnet and non-Magnet nurses. The average number of patients on the last shift was 5.1. The number of patients cared for was not significantly correlated to missed care; however, nurses' perceptions of better staffing adequacy, teamwork and job satisfaction were. Nurses who intended to leave (25.5%) missed more care. Intention to leave and access to resources predicted missed care. CONCLUSION: This appears to be the first study examining the relationship between structural empowerment and missed care, demonstrating that higher empowerment was related to greater nurse work effectiveness and improved care delivery. Work environment factors, specifically subjective perceptions of staffing and resource adequacy, were linked to missed care, while nurse-patient ratio was not. Subjective factors may contribute more to missed care than is recognised. IMPLICATIONS: Creating and sustaining empowering work environments, ensuring resource adequacy and enhancing factors that promote retention may reduce missed care. No patient/public contribution. FINANCIAL CONFLICTS OF INTEREST: None. REPORTING METHOD: STROBE checklist for cross-sectional studies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".