Nursing-Sensitive Outcomes Data Collection in Acute Care and Long-Term-Care Settings
Bibliographic record
Abstract
BACKGROUND: Most administrative databases do not contain good information about nursing-sensitive outcomes. OBJECTIVES: To determine (a) the reliability of the instruments measuring nursing-sensitive outcomes, (b) whether the outcome measures are sensitive to changes in patients' health, and (c) whether the outcome measures are associated with nursing interventions. METHODS: The sample consisted of 890 patients from acute care hospitals and long-term-care facilities. A repeated measures design was used. Functional status was assessed on admission and discharge using Minimum Data Set 2.0 items. Symptom (pain, nausea, dyspnea, fatigue) frequency and severity were assessed with 4-point and 11-point numeric scales, respectively. Therapeutic self-care was assessed on discharge from acute care. Nursing interventions were assessed by documentation review. RESULTS: The outcome measures demonstrated very good interrater reliability with weighted Kappa ranging from .64 to .93. The internal consistency reliability was high for functional status and therapeutic self-care. The outcome tools were sensitive to change in patient condition. Select nursing interventions were related to functional status, therapeutic self-care, and symptom outcomes. DISCUSSION: The findings suggest that nurses are able to collect data on nursing-sensitive patient outcomes in a reliable and valid way.
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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 | 0.099 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".