Disinfection of the Radiologist Workstation and Radiologist Hand Hygiene: A Single Institution Practice Quality Improvement Project
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the workstation disinfection rates and hand hygiene of radiologists and trainees at shared departmental workstations and assess the impact of education and reminder placards on daily habits. METHODS: A 10-question survey was administered to all staff radiologists, fellows, and residents at our institution. The questions pertained to workstation disinfection, hand hygiene habits, and accessibility to disinfectant wipes and hand sanitizer stations. Subsequently, a short educational PowerPoint presentation was emailed to the department and small reminder placards were placed at each workstation. A follow-up survey was administered. Chi-square and Wilcoxon signed-rank tests were used to analyse the results. RESULTS: The percentage of participants who disinfect their workstations 1-2 times/week, 3-4 times/week or everyday increased from 53.4% (45 of 84 participants) to 74.3% (55 of 74 participants; P = .01), while the number who disinfect their workstation <1 time/week or never decreased from 46.4% (39 of 84 participants) to 25.7% (19 of 74 participants; P = .01). Hand washing before working at the workstation increased from 41.6% (35 of 84 participants) to 48.7% (36 of 74 participants; P = .76) and hand washing after working at the workstation increased from 50.0% (42 of 84 participants) to 56.8% (42 of 74 participants; P = .49). CONCLUSIONS: At our institution, the implementation of daily reminder placards at each workstation and the administration of an educational PowerPoint presentation improved the rate of radiologist workstation disinfection.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".