Bibliographic record
Abstract
Adequate hand hygiene is often considered as the most effective method of reducing the rates of hospital-acquired infections, which are one of the major causes of increased cost, morbidity, and mortality in healthcare. Electronic monitoring technologies provide a promising direction for achieving sustainable hand hygiene improvement by introducing the elements of automated feedback and creating the possibility to automatically collect individual hand hygiene performance data. The results of the multiphase testing of an automated hand hygiene reminding and monitoring system installed in a complex continuing care setting are presented. The study included a baseline Phase 1, with the system performing automated data collection only, a preintervention Phase 2 with hand hygiene status indicator enabled, two intervention Phases 3 and 4 with the system generating hand hygiene reminding signals and periodic performance feedback sessions provided, and a postintervention Phase 5 with only hand hygiene status indicator enabled and no feedback sessions provided. A significant increase in hand hygiene performance observed during the first intervention Phase 3 was sustained over the second intervention Phase 4, with the postintervention phase also indicating higher hand hygiene activity rates compared with the preintervention and baseline phases. The overall trends observed during the multiphase testing, the factors affecting acceptability of the automated hand hygiene monitoring system, and various strategies of technology deployment are discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".