Charting the course for American Nurses Credentialing Center–Approved perioperative nurse-sensitive indicators
Bibliographic record
Abstract
The current health care environment, with increasing public awareness of and attention to patient safety, mandates the delivery of exceptional quality care. To meet the health care requisites of the perioperative patient population, clinical nurses have identified the need for nurse-sensitive clinical indicators for this setting. We describe the strategies used to identify, obtain American Nurses Credentialing Center approval for, and integrate nurse-sensitive indicators into the perioperative setting to advance a Magnet culture. Prior to this, nurse-sensitive indicators for the perioperative setting that enabled nurses to monitor and improve patient care outcomes, in accordance with the standards of a Magnet-recognized hospital, had not been formally established. A review of the literature yielded a list of potential metrics, which included normothermia, patient falls with harm, and retained surgical items. Methodology and data collection processes for these metrics were established, facilitating quarterly Nursing Dashboards and collaboration among nurses to improve patient outcomes. This groundbreaking initiative enables nurses to routinely evaluate whether the structures and processes of care effectively yield quality outcomes. This foundational work has broader implications for nursing practice, because these quality metrics can easily be translated into perioperative settings in other health care organizations.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".