Failure to rescue as a nurse-sensitive indicator
Bibliographic record
Abstract
PROBLEM: The aim of this concept analysis was to clarify failure to rescure as a nurse-sensitive indicator. Although the concept of failure to rescue as a nurse-sensitive outcome has appeared in the nursing literature for over a decade, conceptual clarity is needed to address its variable and ambiguous use in health care. METHODS: Walker and Avant's eight-stage method of concept analysis was used to explore the concept of failure to rescue in nursing practice. Twenty-one papers were retrieved from Cumulative Index of Nursing and Allied Health Literature (CINAHL) and MEDLINE databases and selected for review and synthesis. RESULTS: Failure to rescue as a nurse-sensitive indicator was found to be a "failing to rescue" process characterized by a cascade of events, including four key attributes: (1) errors of omission in care, (2) failure to recognize changes in patient condition, (3) failure to communicate changes, and (4) failures in clinical decision making. CONCLUSIONS: Nurses have a pivotal role in "failing to rescue" through early recognition, escalation, and intervention of subtle changes signaling complications. Upstream strategies, such as the use of early warning sign indicators, structured communication, and teamwork, shift the discourse from failure to rescue, to processes in nursing practice of good catch events.
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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 | 0.053 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".