The effect of nursing care delivery models on quality and safety outcomes of care: A cross‐sectional survey study of medical‐surgical nurses
Bibliographic record
Abstract
AIMS: This study examined the effect of two components of a model of nursing care delivery, the mode of nursing care delivery, and skill-mix on: (a) quality of nursing care; and (b) patient adverse events, after controlling for nurse demographics, work environment, and workload factors. DESIGN: A cross-sectional exploratory correlational study that drew on secondary data was conducted. METHODS: Survey data from 416 direct care registered nurses from medical-surgical settings across British Columbia were analysed using hierarchical multiple regression. Larger study data were collected in 2015. RESULTS: Nurses working in a team-based mode reported a greater number of nursing tasks left undone compared with those working in a total patient care. Nurses working in a skill-mix with licensed practical nurses reported a higher frequency of patient adverse events compared with those working in a skill-mix without licensed practical nurses. At higher levels of acuity, nurses in a team-based mode reported a higher frequency of patient adverse events than did nurses in a total patient care. CONCLUSION: Models of nursing care delivery components, mode and skill-mix, influenced quality and safety outcomes. Some of the team-based medical-surgical nurses in British Columbia are not functioning as effective teams. Team building strategies should be used to enhance collaboration among them. IMPACT: Research into redesigning care delivery has typically focused on only one care delivery component at a time. The study findings could have implications for nurses and patients, nursing leadership and policymakers particularly in medical-surgical settings in British Columbia.
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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 | 0.003 | 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".