Educational Interventions to Improve Knowledge Among Nurses in the Prevention of Skin Tears in Hospitalised Adults and Older Adults: A Scoping Review
Bibliographic record
Abstract
To map and synthesise the current literature on educational interventions provided by nurses to nursing professionals to prevent skin tears in adults and older adults. A scoping review based on JBI methodology. Medline, SCOPUS, CINAHL, Web of Science, Science Direct, LILACS, Cochrane Library, ERIC, EMBASE, BVS and J_STAGE were searched from June to November 2022. Grey literature and unpublished studies were included. Studies in English, Spanish or Portuguese were considered, with no year limits. Searches were managed in Endnote and Rayyan. Two independent reviewers screened titles, abstracts and full texts using Population, Concept and Context criteria. Discrepancies were resolved by a third reviewer. Data extraction employed a structured spreadsheet. Of 694 articles retrieved, four met the inclusion criteria, primarily prospective quasi-experimental studies. Two educational modalities were noted: face-to-face classes utilising PowerPoint presentations and online training accessible 24/7 via institutional websites. Key outcomes included improved knowledge levels and reduced skin tear incidence. Nurse-led educational interventions may enhance nursing knowledge and decrease skin tear incidence. Further research is necessary to identify optimal educational approaches and technologies, assess their feasibility and evaluate their direct impact on clinical practice and skin tear prevention and incidence.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".