Facilitators and barriers to pressure injury prevention, management and education: Perspectives from healthcare professionals—A qualitative study
Bibliographic record
Abstract
This study aims to (1) characterize healthcare professionals' (HCPs') experiences related to the prevention and management of pressure injuries (PIs) and (2) explore the educational needs of individuals with a past or current history of PIs and their caregivers from the perspective of HCPs. This is a qualitative descriptive study. HCPs (n = 18) were interviewed using a semi-structured interview guide. Interviews were audio-recorded, transcribed verbatim and coded using NVivo. Three overarching themes encompassing various dimensions were identified: (1) Facilitators related to PI prevention and management, (2) Challenges related to PI prevention and management and (3) Recommendations for improving patient and caregiver PI education. HCPs identified a greater number of challenges than facilitators related to PI care. This study emphasizes the importance of a patient-centred and interprofessional approach to patient education for PI prevention and management. Meaningful interventions focused on the patient may improve health literacy and empower patients and caregivers in PI care. Investing in preventive measures and raising awareness are crucial to reducing PI incidence. The findings have implications for HCPs and researchers seeking to enhance patient care and promote effective PI prevention strategies.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".