Interventions to Improve Hand Hygiene Compliance in the ICU: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: To synthesize the literature describing interventions to improve hand hygiene in ICUs, to evaluate the quality of the extant research, and to outline the type, and efficacy, of interventions described. DATA SOURCES: Systematic searches were conducted in November 2016 using five electronic databases: Medline, CINAHL, PsycInfo, Embase, and Web of Science. Additionally, the reference lists of included studies and existing review papers were screened. STUDY SELECTION: English language, peer-reviewed studies that evaluated an intervention to improve hand hygiene in an adult ICU setting, and reported hand hygiene compliance rates collected via observation, were included. DATA EXTRACTION: Data were extracted on the setting, participant characteristics, experimental design, hand hygiene measurement, intervention characteristics, and outcomes. Interventional components were categorized using the Behavior Change Wheel. Methodological quality was examined using the Downs and Black Checklist. DATA SYNTHESIS: Thirty-eight studies were included. The methodological quality of studies was poor, with studies scoring a mean of 8.6 of 24 (SD= 2.7). Over 90% of studies implemented a bundled intervention. The most frequently employed interventional strategies were education (78.9%), enablement (71.1%), training (68.4%), environmental restructuring (65.8%), and persuasion (65.8%). Intervention outcomes were variable, with a mean relative percentage change of 94.7% (SD= 195.7; range, 4.3-1155.4%) from pre to post intervention. CONCLUSIONS: This review demonstrates that best practice for improving hand hygiene in ICUs remains unestablished. Future research employing rigorous experimental designs, careful statistical analysis, and clearly described interventions is important.
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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 | 0.023 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".