Secondary measures of hand hygiene performance in health care available with continuous electronic monitoring of individuals
Bibliographic record
Abstract
BACKGROUND: Hand hygiene (HH) compliance in health care is usually measured against versions of the World Health Organization's "Your 5 Moments" guidelines using direct observation. Such techniques result in small samples that are influenced by the presence of an observer. This study demonstrates that continuous electronic monitoring of individuals can overcome these limitations. METHODS: An electronic real-time prompting system collected HH data on a musculoskeletal rehabilitation unit for 12 weeks between October 2016 and October 2017. Aggregate and professional group scores and the distributions of individuals' performance within groups were analyzed. Soiled utility room exits were monitored and compared with performance at patient rooms. Duration of patient room visits and the number of consecutive missed opportunities were calculated. RESULTS: Overall, 76,130 patient room and 1,448 soiled utility room HH opportunities were recorded from 98 health care professionals. Aggregate unit performance for patient and soiled utility rooms were both 67%, although individual compliance varied greatly. The number of hand wash events that occurred while inside patient rooms increased with longer visits, whereas HH performance at patient room exit decreased. Eighty-three percent of missed HH opportunities occurred as part of a series of missed events, not in isolation. CONCLUSIONS: Continuous collection of HH data that includes temporal, spatial, and personnel details provides information on actual HH practices, whereas direct observation or dispenser counts show only aggregate trends.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".