1500Predictors of Hand Hygiene in the Emergency Department (ED): Impact of ED Crowding
Bibliographic record
Abstract
Background. Hand hygiene (HH) is not well studied in the emergency department (ED). In other settings, increased workload is associated with reduced compliance. We tested this hypothesis in the ED. Methods. ED HH compliance was tracked at our facility by direct observation from January 2011 to October 2013. Daily ED patient volumes, staffing levels and mean time to MD assessment (TMDA) were used as measures of ED crowding. Predictors associated with compliance in univariate analysis (p < 0.2) were included in a multivariate logistic regression model. Results. Average compliance was 29% (325/1116): 10% before aseptic procedures, 22% before patient/environmental contact, 26% after body fluid exposure and 37% after patient/environmental contact. Alcohol-based sanitizer was used 66% (215/325) of the time. Nurse staffing levels and patient volumes were not associated with compliance but TMDA was. Compliance was 38% for TMDA in the first quartile and 25% for TMDA in the fourth quartile (figure). Predictors of reduced compliance that remained significant (p < 0.05) in the multivariate model included: longer TMDA; HH prior to patient/environmental contact or aseptic procedures (vs HH after contact); and professional designation of ‘housekeeping' or ‘other' (vs nursing). Conclusion. HH compliance in the ED was low. Soap/water are still used for 33% of HH. Increased TMDA and indication for HH were the strongest predictors of compliance. The drop in compliance seen with increasing TMDA indicates that ED crowding contributes to poor ED HH. Strategies to reduce the time required for HH in the ED (including optimal placement of dispensers or use of personal dispensers) and improve workflow practices are logical targets for improvement. Disclosures. All authors: No reported disclosures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".