Hero Program: A data-driven reward system to improve hand hygiene
Bibliographic record
Abstract
BACKGROUND: Healthcare-associated infections compromise patient outcomes and pose a burden on healthcare systems worldwide. Despite widespread awareness of the critical role of Hand Hygiene (HH) in preventing healthcare-associated infections, compliance among healthcare workers remains suboptimal. This study evaluates a reward program called the Hero Program which is designed to incentivize and sustain HH practices through positive reinforcement. METHODS: The program used data from an electronic HH prompting system that has been installed in an inpatient unit in Toronto Rehabilitation Institute - University Health Network for 3years. A scoring algorithm was implemented to weigh individual HH compliance rates, considering workload and rewarding consistency. Daily winners were selected based on their scores and received gift card rewards. RESULTS: The analysis of data from 61 caregivers and more than 566,000 records for approximately 2.5years indicates that the Hero Program led to an 11.45% increase in HH compliance after 120days of implementation. This is a promising finding, suggesting that the program was effective in promoting behavior change early on. Compliance rates continued to improve over time, reaching 94% 1year later. CONCLUSIONS: This sustained improvement suggests that the program had a long-lasting positive impact on HH practices in the unit.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".