Pressure ulcers: implementation of evidence‐based nursing practice
Bibliographic record
Abstract
AIMS: A 2-year project was carried out to evaluate the use of multi-component, computer-assisted strategies for implementing clinical practice guidelines. This paper describes the implementation of the project and lessons learned. The evaluation and outcomes of implementing clinical practice guidelines to prevent and treat pressure ulcers will be reported in a separate paper. BACKGROUND: The prevalence and incidence rates of pressure ulcers, coupled with the cost of treatment, constitute a substantial burden for our health care system. It is estimated that treating a pressure ulcer can increase nursing time up to 50%, and that treatment costs per ulcer can range from US$10,000 to $86,000, with median costs of $27,000. Although evidence-based guidelines for prevention and optimum treatment of pressure ulcers have been developed, there is little empirical evidence about the effectiveness of implementation strategies. METHOD: The study was conducted across the continuum of care (primary, secondary and tertiary) in a Canadian urban Health Region involving seven health care organizations (acute, home and extended care). Trained surveyors (Registered Nurses) determined the prevalence and incidence of pressure ulcers among patients in these organizations. The use of a computerized decision-support system assisted staff to select optimal, evidence-based care strategies, record information and analyse individual and aggregate data. RESULTS: Evaluation indicated an increase in knowledge relating to pressure ulcer prevention, treatment strategies, resources required, and the role of the interdisciplinary team. Lack of visible senior nurse leadership; time required to acquire computer skills and to implement new guidelines; and difficulties with the computer system were identified as barriers. CONCLUSIONS: There is a need for a comprehensive, supported and sustained approach to implementation of evidence-based practice for pressure ulcer prevention and treatment, greater understanding of organization-specific barriers, and mechanisms for addressing the barriers.
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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 | 0.045 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".