Significant heterogeneity in hand hygiene compliance between different physician groups
Bibliographic record
Abstract
Designated auditors were trained to perform HH audits in all inpatient and long term care units of our 1185-bed academic tertiary medical centre. The auditors used a standardized validated tool to audit HCW’s HH during their patient interactions. The physician group was separated into 3 categories according to their seniority: staff physician, resident/fellow physician and medical student. When the auditors are unsure which category a physician belongs to, they ask for verbal confirmation. The HH compliance of the three physician groups was compared. Between Oct 2010 and Feb 2011, a total of 8337 HH opportunities were observed. 1292 (15%) opportunities were observed in the physician group: 486 opportunities were observed in staff physicians, 693 in resident/fellow physicians, and 113 in medical students. The overall average hospital compliance was 79%; overall physician group compliance was 65%. Among the physician group, staff physicians compliance was 71%, resident/fellow physicians was 61% and medical students was 67%. The difference between staff physicians and resident/fellow physicians was statistically significant. There is marked heterogeneity in HH compliance between staff physicians and resident/fellow physicians. The capability to detect such differences is important to direct compliance improvement efforts, with the goal of improving overall HH compliance.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".