Identification badges: a potential fomite?
Bibliographic record
Abstract
BACKGROUND: Staff identification badges are mandatory in all hospitals. The purpose of this study was to assess microbial contamination of identification badges at a Canadian tertiary centre. Risk factors for badge contamination were also investigated. METHODS: Badges were cultured from 118 subjects including secretaries, physicians, nurses, and allied health workers. Subjects also completed a demographic questionnaire. Badge contamination was analyzed according to profession, workplace, duration of badge use, presence of a plastic cover, how the badge was worn, and cleaning frequency. RESULTS: 13.6% of the badges were contaminated with significant pathogens. S. aureus was isolated in 6.8% of the badges, gram-negative bacilli in 5.9%. Contamination was highest in nurses (21.4% versus 9.4-14.3% in other professions) and in the ICU (22.6% versus 8.3%-14.3% at other locations). Neither association was statistically significant. Covered and non-covered badges had similar contamination rates (12% and 17.1%) as did badges worn around the neck compared with those worn clipped to clothing (13.0% versus 14.6%). Contamination of recently cleaned badges was not statistically different from those that had not. CONCLUSION: Identification badges do not appear to be a major reservoir for pathogenic organisms. Badges can, however, harbour disease-causing organisms and should be cleaned regularly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".