Factors in facilitating an organisational culture to prevent pressure ulcers among older adults in health-care facilities
Bibliographic record
Abstract
OBJECTIVE: Despite the availability of high-quality clinical practice guidelines, pressure ulcers (PU) continue to develop among older adults in acute and long-term health-care facilities. Except during acute medical crisis or near end-of-life, most PUs are preventable and their development is a health-care quality indicator. The aim of this study was to understand which factors facilitate pressure ulcer prevention among adults over 65 years-of-age receiving care in health-care facilities. METHOD: A critical literature review from three scholarly databases examined components of organisational culture associated with PU prevention. Research papers involving adults >65 years-of-age who were admitted to acute and long-term health-care facilities with PU prevention programmes between 2010 and 2017 were included. A secondary manual search included literature discussing health-care organisational culture, with a total of 41 articles reviewed. RESULTS: Based on a synthesis of this literature, the Factors Facilitating Pressure Ulcer Prevention Model was developed to depict five multilevel factors for PU prevention among older adults in health-care facilities. These five factors are: senior leadership, education, ongoing quality improvement, clinical practice, and unit level champions. CONCLUSION: Ongoing prioritisation of these factors sustains PU prevention and assists health-care facilities to redefine their culture, expand education programmes, and promote accountability to improve health outcomes of older adults receiving care.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".