Do physicians clean their hands? Insights from a covert observational study
Bibliographic record
Abstract
Physicians are notorious for poor hand hygiene (HH) compliance. We wondered if lower performance by physicians compared with other health professionals might reflect differences in the Hawthorne effect. We introduced covert HH observers to see if performance differences between physicians and nurses decreased and to gain further insights into physician HH behaviors. Following training and validation with a hospital HH auditor, 2 students covertly measured HH during clinical rotations. Students rotated off clinical services every week to increase exposure to different providers and minimize risk of exposing the covert observation. We compared covertly measured HH compliance with data from overt observation by hospital auditors during the same time period. Covert observation produced much lower HH compliance than recorded by hospital auditors during the same time period: 50.0% (799/1597) versus 83.7% (2769/3309) (P < 0.0002). The difference in physician compliance between hospital auditors and covert observers was 19.0% (73.2% vs 54.2%); for nurses this difference was much higher at 40.7% (85.8% vs 45.1%) (P < 0.0001). Physician trainees showed markedly better compliance when attending staff cleaned their hands compared with encounters when attending did not (79.5% vs 18.9%; P < 0.0002). Our study suggests that traditional HH audits not only overstate HH performance overall, but can lead to inaccurate inferences about performance by professional groupings due to relative differences in the Hawthorne effect. We suggest that future improvement efforts will rely on more accurate HH monitoring systems and strong attending physician leadership to set an example for trainees. Journal of Hospital Medicine 2015;11:862-864. © 2015 Society of Hospital Medicine.
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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 | 0.010 | 0.052 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".