Point of care hand hygiene—where's the rub? A survey of US and Canadian health care workers' knowledge, attitudes, and practices
Bibliographic record
Abstract
BACKGROUND: Hand hygiene at the point of care is recognized as a best practice for promoting compliance at the moments when hand hygiene is most critical. The objective of this study was to compare knowledge, attitudes, and practices of US and Canadian frontline health care personnel regarding hand hygiene at the point of care. METHODS: Physicians and nurses in US and Canadian hospitals were invited to complete a 32-question online survey based on evidence supporting point of care hand hygiene. Eligible health care personnel were in direct clinical practice at least 50% of the time. RESULTS: Three hundred fifty frontline caregivers completed the survey. Among respondents, 57.1% were from the United States and 42.9% were from Canada. Respondents were evenly distributed between physician and nurses. The US and Canadian respondents gave identical ranking to their perceived barriers to hand hygiene compliance. More than half of the respondents from both the United States and Canada agreed or strongly agreed that they would be more likely to clean their hands when recommended if alcohol-based handrub was closer to the patient. CONCLUSION: This survey demonstrates that similarities between Canada and the United States were more common than not, and the survey raises, or suggests, potential knowledge gaps that require further illumination.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".