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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".