[Effect of the guideline implementation "Risk assessment and prevention of pressure ulcers" of the Registered Nurses'Association of Ontario (RNAO).]
Bibliographic record
Abstract
OBJECTIVE: The Best Practice Spotlight Organizations Program is being developed in Spain to reduce the variability of clinical practice by implementing clinical practice guidelines from the Registered Nurses' Association of Ontario. This study described the results of the implementation of the guide "Risk assessment and prevention of pressure ulcers". METHODS: We carried out a retrospective observational study (2015-2018) at the Hospital Universitario Virgen de las Nieves on 4,464 patients from 22 hospitalization units, analyzing type of unit, risk assessment, preventive measures, origin and category of ulcers. Descriptive analysis and contingency tables were performed with the Chi-square statistic p<0.05. RESULTS: The patients at risk were 62.2% in medical units, 53.4% in surgical units and 90% in intensive care. The application of preventive measures was 67.9%, 60.2% and 92.1% (respectively) for each unit. In medical units, 13.1% of pressure ulcers were identified, of which 68.1% were present at the time of admission. While in surgical units and intensive care they developed during hospitalization (60.8% and 88.9% respectively) (p<0.001). The presence of ulcers seemed to show a decreasing trend in the years analyzed (19.6% to 11.2%). CONCLUSIONS: There are favorable environments for implantation (medical units and intensive care) that reflect a higher level of risk assessment, use of pressure management surfaces and a decrease in prevalence. The recommendations have not been implemented homogeneously, with differences depending on the type of unit.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".