Impact of hospital nursing care on 30‐day mortality for acute medical patients
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Bibliographic record
Abstract
AIM: This paper reports on structures and processes of hospital care influencing 30-day mortality for acute medical patients. BACKGROUND: Wide variation in risk-adjusted 30-day hospital mortality rates for acute medical patients indicates that hospital structures and processes of care affect patient death. Because nurses provide the majority of care to hospitalized patients, we propose that structures and processes of nursing care have an impact on patient death or survival. METHOD: A model hypothesizing the impact of nursing-related hospital care structures and processes on 30-day mortality was tested. Patient data from the Ontario, Canada Discharge Abstract Database 2002-2003, nurse data from the Ontario Nurse Survey 2003, and hospital staffing data from the Ontario Hospital Reporting System 2002-2003 files were used to develop indicators for variables hypothesized to impact 30-day mortality. Two multiple regression models were implemented to test the model. First, all variables were forced to enter the model simultaneously. Second, backward regression was implemented. FINDINGS: Using backward regression, 45% of variance in risk-adjusted 30-day mortality rates was explained by eight predictors. Lower 30-day mortality rates were associated with hospitals that had a higher percentage of Registered Nurse staff, a higher percentage of baccalaureate-prepared nurses, a lower dose or amount of all categories of nursing staff per weighted patient case, higher nurse-reported adequacy of staffing and resources, higher use of care maps or protocols to guide patient care, higher nurse-reported care quality, lower nurse-reported adequacy of manager ability and support, and higher nurse burnout. CONCLUSION: Just as hospitals and clinicians caring for patients focus carefully on completing accurate diagnosis and appropriate and effective interventions, so too should hospitals carefully plan and manage structures and processes of care such as the proportion of Registered Nurses in the staff mix, percentage of baccalaureate-prepared nurses, and routine use of care maps to minimize unnecessary patient death.
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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) | 0.001 | 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 | 0.000 |
| 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