Are minimum nurse-to-patient staffing ratios needed in hospitals? An observational study in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVE: To evaluate staffing conditions, patient outcomes, quality of care, patient safety and nurse job outcomes in British Columbia (BC), Canada hospitals. DESIGN: Cross-sectional study of 58 hospitals in BC with surveys of nurses and independent measures of patient outcomes. SETTING: 58 hospitals in BC. PARTICIPANTS: 6685 hospital-based nurses working in a direct patient care role. EXPOSURES: Hospital-wide and unit-specific patient-to-nurse staffing ratios derived from registered nurse reports of how many patients and how many nurses were on their unit during their last shift worked. MAIN OUTCOMES AND MEASURES: Objective patient outcome measures included the Hospital Standardized Mortality Ratio (HSMR) and 30-day Readmission Rate, from 2022 to 2023 Canadian Institute for Health Information data. Nurses4All@BC provided data from 2024 using validated items on multiple measures (eg, nurse burnout, missed health breaks, intentions to leave, quality and safety measures such as culture of patient safety, quality of nursing care, missed nursing care). RESULTS: Burnout (59.4%), missed health breaks (41.7%), job dissatisfaction (36.0%), intentions to leave (19.3%) and patient outcomes (HSMR mean 95.4, median 96.0, range 26-180; readmission rate mean 10.0%, median 9.5%, range 7.9%-13.8%) were high and varied across hospitals. 68.3% of nurses reported there were not enough staff, and 77.3% reported their workloads were unsafe for patients. 60.6% of nurses gave their hospital an unfavourable patient safety rating. More patients per nurse were associated with poorer hospital mortality and readmission rates, poorer job outcomes for nurses, more adverse events for patients, less favourable ratings of quality of care and patient safety, more missed nursing care and poorer ratings of staffing adequacy and management. CONCLUSIONS: Given the variability in staffing, quality and patient outcomes across BC hospitals, the implementation of a minimum nurse-to-patient ratio policy has the potential to improve patient care safety and retention of nurses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".