Workplace Predictors of Quality and Safe Patient Care Delivery Among Nurses Using Machine Learning Techniques
Bibliographic record
Abstract
BACKGROUND: Working in unhealthy environments is associated with negative nurse and patient outcomes. Previous body of evidence in this area is limited as it investigated only a few factors within nurses' workplaces. PURPOSE: The purpose of this study was to identify the most important workplace factors predicting nurses' provision of quality and safe patient care using a 13-factor measure of workplace conditions. METHODS: A cross-sectional correlational survey study involving 4029 direct care nurses in British Columbia was conducted using random forest data analytics methods. RESULTS: Nurses' reports of healthier workplaces, particularly workload management, psychological protection, physical safety and engagement, were associated with higher ratings of quality and safe patient care. CONCLUSION: These workplace conditions are perceived to impact patient care through influencing nurses' mental health. To ensure a high standard of patient care, data-driven policies and interventions promoting overall nurse mental health and well-being are urgently required.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".