Developing and Testing an International Audit of Nursing Quality Indicators for Older Adults With Fragility Hip Fracture
Bibliographic record
Abstract
BACKGROUND: Fragility hip fracture in older adults often has poor outcomes, but these outcomes can be improved with attention to specific quality care indicators. PURPOSE: The International Collaboration of Orthopaedic Nursing (ICON) developed an audit process to identify the extent to which internationally accepted nursing quality care indicators for older adults with fragility hip fracture are reflected in policies, protocols, and processes guiding acute care. METHODS: A data abstraction tool was created for each of 12 quality indicators. Data were collected using a mixed-methods approach with unstructured rounds. A rationale document providing evidence for the quality indicators and a user evaluation form were included with the audit tool. A purposeful sample of 35 acute care hospitals representing 7 countries was selected. RESULTS: Thirty-five hospitals (100%) completed the survey. Respondents viewed the content as relevant and applicable for the defined patient population. Although timing and frequency of implementation varied among and within countries, the identified quality indicators were reflected in the majority of policies, protocols, or processes guiding care in the hospitals surveyed. CONCLUSION: Developing and testing an audit of nurse-sensitive quality indicators for older adults with fragility hip fracture demonstrate international consensus on common core best practices to ensure optimal acute care.
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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 | 0.115 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".