Barriers and enablers to implementation of pressure injury prevention in hospitalized adults: a mixed methods systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of the proposed systematic review is to determine the barriers and enablers (or facilitators) to the implementation of pressure injury prevention among adults receiving care in the hospital setting. INTRODUCTION: Hospital-acquired pressure injuries are preventable; however, they remain an ongoing safety and quality health care concern in many countries. There are various evidence-based preventative interventions for pressure injuries, but their implementation in clinical practice is limited. An understanding of the different factors that support (enablers or facilitators) and inhibit (barriers) the implementation of these interventions from different perspectives is important, so that targeted strategies can be incorporated into implementation plans. INCLUSION CRITERIA: This review will include quantitative, qualitative, and mixed methods studies that investigate barriers and/or enablers in relation to hospital-acquired pressure injury prevention in hospitalized adults. Only English publications will be considered, with no publication date restrictions. METHODS: The systematic review will be conducted in accordance with the JBI methodology for mixed methods systematic review. Published studies will be searched in PubMed, CINAHL, Embase, PsycINFO and Scopus. Gray literature will also be considered. Critical appraisal and data extraction will be performed using standardized tools, followed by data transformation. Data synthesis will follow the convergent integrated approach.
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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 | 0.133 | 0.101 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.016 | 0.016 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.057 | 0.009 |
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".