Identifying interventions to improve hand hygiene compliance in the intensive care unit through co-design with stakeholders
Bibliographic record
Abstract
Background: Despite the effectiveness of hand hygiene (HH) for infection control, there is a lack of robust scientific data to guide how HH can be improved in intensive care units (ICUs). The aim of this study is to use the literature, researcher, and stakeholder opinion to explicate potential interventions for improving HH compliance in the ICU, and provide an indication of the suitability of these interventions. Methods: A four-phase co-design study was designed. First, data from a previously completed systematic literature review was used in order to identify unique components of existing interventions to improve HH in ICUs. Second, a workshop was held with a panel of 10 experts to identify additional intervention components. Third, the 91 intervention components resulting from the literature review and workshop were synthesised into a final list of 21 hand hygiene interventions. Finally, the affordability, practicability, effectiveness, acceptability, side-effects/safety, and equity of each intervention was rated by 39 stakeholders (health services researchers, ICU staff, and the public). Results: Ensuring the availability of essential supplies for HH compliance was the intervention that received most approval from stakeholders. Interventions involving role models and peer-to-peer accountability and support were also well regarded by stakeholders. Education/training interventions were commonplace and popular. Punitive interventions were poorly regarded. Conclusions: Hospitals and regulators must make decisions regarding how to improve HH compliance in the absence of scientific consensus on effective methods. Using collective input and a co-design approach, the guidance developed herein may usefully support implementation of HH interventions that are considered to be effective and acceptable by stakeholders.
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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 | 0.198 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".