Comprehensive Programs for Preventing Pressure Ulcers
Bibliographic record
Abstract
In Brief PURPOSE: To enhance the learner’s competence in pressure ulcer (PrU) prevention through a literature review of comprehensive programs. TARGET AUDIENCE: This continuing education activity is intended for physicians and nurses with an interest in skin and wound care. OBJECTIVES: After participating in this educational activity, the participant should be better able to: 1. Analyze the findings of the PrU prevention program studies found in the literature review. 2. Apply research findings to clinical practice. OBJECTIVE: The objective of this study was to examine the evidence supporting the combined use of interventions to prevent pressure ulcers (PrUs) in acute care and long-term-care facilities. DESIGN: A systematic review of the literature describing multifaceted PrU prevention programs was performed. Articles were included if they described an intervention implemented in acute care settings or long-term-care facilities, incorporated more than 1 intervention component, involved a multidisciplinary team, and included information about outcomes related to the intervention. MAIN RESULTS: Twenty-four studies were identified. Recurring components used in the development and implementation of PrU prevention programs included preparations prior to the start of a program, PrU prevention best practices, staff education, clinical monitoring and feedback, skin care champions, and cues to action. Ten studies reported PrU prevalence rates; 9 of them reported decreased prevalence rates at the end of their programs. Of the 6 studies reporting PrU incidence rates, 5 reported a decrease in incidence rates. Four studies measured care processes: 1 study reported an overall improvement; 2 studies reported improvement on some, but not all, measures; and 1 study reported no change. CONCLUSIONS: There is a growing literature describing multipronged, multidisciplinary interventions to prevent PrUs in acute care settings and long-term-care facilities. Outcomes reported in these studies suggest that such programs can be successful in reducing PrU prevalence or incidence rates. However, to strengthen the level of evidence, sites should be encouraged to rigorously evaluate their programs and to publish their results. This continuing education activity discusses the evidence supporting the combined use of interventions to prevent pressure ulcers in acute-care and long-term-care facilities.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".