The effect of nursing education on dressing usage and wear time in the home care setting
Bibliographic record
Abstract
Background: A vast amount of wound care occurs in the community, yet varying degrees of knowledge and education means wound dressing choice and usage is not always being optimised. Aim: This study aimed to investigate the effects of different forms of education alongside the introduction of a wound dressing on nurses’ frequency of dressing changes in the home care setting. Methods: A comparisoned design of three approaches to explore the impact of wound care education was undertaken across three district nursing teams. Semi-structured interviews with 10 health care professionals explore the impact of education on nurses' clinical practice, decision making and confidence. Results: Reductions in overall wound dressing change frequency were observed in the group receiving the education package with training, compared to the control group. Dressing-specific education enabled staff to trust and feel confident about using the dressings, whilst the wound care education enabled participants to apply new knowledge to their clinical practice. Discussion: This study highlights the importance of how education can provide valuable solutions to reducing unnecessary dressing changes, reducing nursing visits and dressing use, and supporting clinicians to feel more confident in their practice. Product-specific education and general wound care education combined can help to build confidence and support positive changes in practice.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".