Wound hygiene survey: awareness, implementation, barriers and outcomes
Bibliographic record
Abstract
OBJECTIVE: In light of the COVID-19 pandemic, which has resulted in changes to caseload management, access to training and education, and other additional pressures, a survey was developed to understand current awareness and implementation of the wound hygiene concept into practice one year on from its dissemination. Barriers to implementation and outcomes were also surveyed. METHOD: projects team, in consultation with ConvaTec, and distributed globally via email and online; the survey was open for just over 12 weeks. Due to the exploratory nature of the research, non-probability sampling was used. The authors reviewed the outputs of the survey to draw conclusions from the data, with the support of a medical writer. RESULTS: There were 1478 respondents who agreed to the use of their anonymised aggregated data. Nearly 90% were from the US or UK, and the majority worked in wound care specialist roles, equally distributed between community and acute care settings; 66.6% had been in wound care for more than 8 years. The respondents work across the spectrum of wound types. More than half (57.4%) had heard of the concept of wound hygiene, of whom 75.3% have implemented it; 78.7% answered that they 'always' apply wound hygiene and 20.8% 'sometimes' do so. The top three barriers to adoption were confidence (39.0%), the desire for more research (25.7%) and competence (24.8%). Overall, following implementation of wound hygiene, 80.3% reported that their patients' healing rates had improved. CONCLUSION: Respondents strongly agreed that implementing wound hygiene is a successful approach for biofilm management and a critical component for improving wound healing rates in hard-to-heal wounds. However, the barriers to its uptake and implementation demonstrate that comprehensive education and training, institutional support for policy and protocol changes, and more clinical research are needed to support wound hygiene.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".