Which priority indicators to use to evaluate nursing care performance? A discussion paper
Bibliographic record
Abstract
AIMS: A discussion of an optimal set of indicators that can be used on a priority basis to assess the performance of nursing care. BACKGROUND: Recent advances in conceptualization of nursing care performance, exemplified by the Nursing Care Performance Framework, have revealed a broad universe of potentially nursing-sensitive indicators. Organizations now face the challenge of selecting, from this universe, a realistic subset of indicators that can form a balanced and common scorecard. DESIGN: Discussion paper drawing on a systematic assessment of selected performance indicators. DATA SOURCES: Previous works, based on systematic reviews of the literature published between 1990 - 2014, have contributed to the development of the Nursing Care Performance Framework. These works confirmed a robust set of indicators that capture the universe of content currently supported by the scientific literature and cover all major areas of nursing care performance. Building on these previous works, this study consisted in gathering the specific evidence supporting 25 selected indicators, focusing on systematic syntheses, meta-analyses and integrative reviews. IMPLICATIONS FOR NURSING: This study has identified a set of 12 indicators that have sufficient breadth and depth to capture the whole spectrum of nursing care and that could be implemented on a priority basis. CONCLUSIONS: This study sets the stage for new initiatives aiming at filling current gaps in operationalization of nursing care performance. The next milestone is to set up the infrastructure required to collect data on these indicators and make effective use of them.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".