Systemwide Practice Change Program to Combat Hospital-Acquired Pressure Injuries
Bibliographic record
Abstract
BACKGROUND: Considerable evidence exists on how to prevent hospital-acquired pressure injuries (HAPIs). However, processes employed to implement evidence play a significant role in influencing outcomes. PROBLEM: One Australian health district experienced a substantial increase in HAPIs over a 5-year period (by almost 60%) that required a systemwide practice change. APPROACH: This article reports on the people, processes, and learnings from using the Promoting Action on Research Implementation in Health Services (PARiHS) framework taking into account the evidence, context, and facilitation to address HAPIs. OUTCOMES: Applying this approach resulted in a significant decrease in pressure injuries and positive practice change, leading to improved patient outcomes in a shorter time frame than previous strategies. CONCLUSION: Processes guided by the PARiHS enhanced the effectiveness of translating evidence into practice and positively assisted clinicians to promote optimal patient care. This approach is transferrable to other health care settings.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".