Factors Associated With the Management of Pressure Injuries at the End of Life
Bibliographic record
Abstract
Pressure injuries are a major problem in all health care settings. The incidence of pressure injuries at the end of life is as high as 58% in some facilities, and there is little consensus on how pressure injuries are managed at the end of life. A scoping review was conducted, to investigate what is known of the factors associated with the management of pressure injuries at the end of life. Literature was sourced from several databases. A total of 1760 potential sources were identified; after applying the Population Concept Context inclusion and exclusion criteria, 16 empirical research articles were sourced: 10 were quantitative, 5 were qualitative, and 1 was mixed methods. Studies were published between 2003 and 2021 and originated from Italy, the United States, Australia, Sweden, Brazil, Taiwan, Turkey, and Canada. Key interventions included regular second hourly turning, wound debridement, wound assessment, and application of various wound coverings. The most widely discussed management strategy, regular second hourly turning, proved controversial and inconsistent in practice. There were also inconsistencies with wound assessment, with practice not always following best evidence-based assessment guidelines. Research findings also highlighted issues with prognostication and identification of the end-of-life phase with no consistent tool applied to assist end-of-life pressure injury management decision-making.
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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 | 0.007 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".