Evaluation of evidence-based nursing education of hospital acquired pressure injury prevention in clinical practice: An integrative review
Bibliographic record
Abstract
Background and objective: There are 2.5 million Americans affected by hospital-acquired pressure injuries (HAPI) annually. The objective of this study was to review and synthesize the evidence on competency-based education of interventions to prevent HAPIs and to improve the knowledge-base of nursing to guide quality and safety initiatives for patients.Methods: A total of 30 articles met the inclusion and exclusion criteria. They were reviewed, and the selected articles focused into three main areas: nurse knowledge and education, HAPI prevention improvement, and competency. These articles were examined to comprise a review on the studies that provided the most relevant synchronized data concerning pressure ulcers and competency-based education.Results: Two themes developed during the literature search and analysis of the selected articles. The first theme focused on nurse education programs for the prevention and identification of HAPI, and the second was the need for nurse knowledge and competency in the prevention of HAPI.Conclusions: Appraisal of the literature showed that various HAPI education programs have improved nurses’ knowledge and competency, and decreased HAPI occurrences. Future research should focus on identifying and reinforcing standardized professional competency-based education to create a culture of success, and ensure consistently high quality care and safe outcomes for patients.
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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 | 0.017 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".