Antimicrobial stewardship in wound care implementation and measuring outcomes: results of an e-survey
Bibliographic record
Abstract
OBJECTIVE: Antimicrobial resistance (AMR) occurs (as a result of misuse, such as over-prescribing) when certain pathogens fail to respond to treatment with antimicrobials. Consequently, patients can become severely ill and possibly die. A strategy referred to as antimicrobial stewardship (AMS) has been introduced which reduces the impact of this antimicrobial misuse. To explore health professionals' (working in wound care, treating both acute and hard-to-heal wounds) position in terms of the following: awareness of AMS; if they are aware of AMS, if they implement procedures to support its practice; and if they implement AMS, do they measure its impact by and compare pre- and post-implementation? METHOD: An e-survey designed to explore health professionals' awareness of AMS and its implications for wound care. RESULTS: There were 987 respondents to the survey. The majority were specialist wound care nurses, mainly based in the UK or the US and Canada. A high proportion of those surveyed were completely/partially aware (35.1/57.9%, respectively) of AMS, and almost all implemented strategies to reduce antimicrobial prescribing. Of those surveyed, 36% took steps to measure the impact of AMS, and as a result 35.2% reported positive impacts (for example, cost reductions, a reduction in the systemic use of antimicrobials, a reduction in the topical use of antimicrobials and a reduced level of antimicrobial-resistant microorganisms). Challenging aspects of AMS implementation were reported by 33.2% of respondents (for example, poorer clinical outcomes in terms of healing and increased costs). The data highlighted that 40.49% felt that AMS would be 'easy' or 'very easy' to implement while 21.73% felt that AMS would be 'difficult' or 'very difficult' to implement. CONCLUSION: Education strategies need to be devised to raise awareness and support health professionals, including wound care practitioners, to understand and implement effective AMS programmes. Development of clear metrics is required to evaluate the effect of AMS programmes in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".