Strategies to support pressure injury best practices by the inter‐professional team: A systematic review
Bibliographic record
Abstract
Optimal pressure injury (ulcer) management by the inter-professional team requires appropriate health care system and organisational resources, infrastructure, and policies. A systematic review was conducted on pressure injury care-related education and health care system-/organisation-level strategies. A search for relevant articles published between January 2006 and October 2014 was applied to 8 databases. Ultimately, 22 articles pertaining to education and training and 12 articles pertaining to health care system and organisation supports for pressure injury care were included in the systematic review. A lack of pressure injury assessment and management knowledge by health care professionals was an overriding theme in the education literature. Some of the methods preferred for pressure injury education among nurses and physicians included information technology (eg, e-learning) with technology support and the use of high-quality wound pictures. Although the evidence is scarce, the literature did highlight specific system- and organisation-level barriers and enablers that influence practice change, including inter-professional communication and human resource investments. In conclusion, (1) the current evidence on the education and system-level enablers, barriers, and strategies to optimise pressure injury best practices requires further investigation, and (2) multi-faceted, up-stream, evidence-based approaches for pressure injury care are essential to improve health care and patient-related outcomes.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".