Effects of work environments on nurse and patient outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: To determine the relationship between nurses' perceptions of their work environment and quality/risk outcomes for patients and nurses in acute care settings. BACKGROUND: Nurses are leaving the profession as a result of high levels of job dissatisfaction arising from current working conditions. To gain organizational support for workplace improvements, evidence is needed to demonstrate the impact of the work environment on patient care. METHOD: A multi-level design was used to collect data from nurses (n=679) and patients (n=1005) within 61 medical and surgical units in 21 hospitals in Canada. RESULTS: Using multilevel structural equation modelling, the hypothesized model fitted well with the data [χ(2)=21.074, d.f.=10, Comparative Fit Index (CFI)=0.985, Tucker-Lewis Index (TLI)=0.921, Root Mean Square Error of Approximation (RMSEA)=0.041, Standardized Root Mean Square Residual (SRMR) 0.002 (within) and 0.054 (between)]. Empowering workplaces had positive effects on nurse-assessed quality of care and predicted fewer falls and nurse-assessed risks as mediated through group processes. These conditions positively impacted individual psychological empowerment which, in turn, had significant direct effects on empowered behaviour, job satisfaction and care quality. CONCLUSIONS: Empowered workplaces support positive outcomes for both nurses and patients. IMPLICATIONS FOR NURSING MANAGEMENT: Managers employing strategies to create more empowered workplaces have the potential to improve nursing teamwork that supports higher quality care, less patient risk and more satisfied nurses.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it