Formal Caregiver Experiences of Caring for Individuals at Risk for or with a Pressure Injury: A Metasynthesis
Bibliographic record
Abstract
OBJECTIVE: To explore formal caregiver experiences caring for patients with a pressure injury (PI) or who are at risk of developing a PI. DATA SOURCES: In November 2019, the researchers searched CINAHL (Cumulative Index to Nursing and Allied Health Literature) and MEDLINE databases for articles related to caregivers and their experiences with PI prevention and care. STUDY SELECTION: Twenty-eight qualitative studies were included in this review. DATA EXTRACTION: Literature was graded and critiqued with regard to design and research quality and then synthesized utilizing a narrative approach. DATA SYNTHESIS: Four themes were found within the literature: knowledge and education, environment and resources, collaboration and role clarity, and risk assessment. CONCLUSIONS: Across healthcare settings, formal caregivers noted the importance of effective PI knowledge and education. Recognizing both barriers and facilitators to PI prevention and management within the healthcare environment can help decision-makers make informed choices to improve PI management within their settings. In addition, developing interprofessional team skills and relationships, rather than practicing in silos, may have an impact on PI management. Although there are many interventions that reduce PI risk and assist in the management of PIs, not every intervention is appropriate for every healthcare setting. Clinician education on PIs, along with new interventions, could significantly impact the effectiveness of patient 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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".