Organizational and infrastructural risk factors for health care–associated Clostridioides difficile infections or methicillin-resistant Staphylococcus aureus in hospitals
Bibliographic record
Abstract
BACKGROUND: This study explores the infrastructural and organizational risk factors for health care-associated (HCA) Clostridioides difficile infections (CDIs) and methicillin-resistant Staphylococcus aureus (MRSA) in hospitals. METHODS: This is a retrospective observational study involving all eligible inpatient units from 12 hospitals in British Columbia, Canada, from April 1, 2020 to September 16, 2021. The outcomes were the average HCA CDI or MRSA rates. Covariates included, but were not limited to, infection control factors (eg, hand hygiene rate), infrastructural factors (eg, unit age), and organizational factors (eg, hallway bed utilization). Multivariable regression was performed to identify statistically significant risk factors. RESULTS: Older units were associated with higher HCA CDI rates (adjusted relative risk [aRR]: 0.012; 95% confidence interval (CI) [0.004, 0.020]). Higher HCA MRSA rates were associated with decreased hand hygiene rate (aRR: -0.035; 95% CI [-0.063, -0.008]), higher MRSA bioburden (aRR: 9.008; 95% CI [5.586, 12.429]), increased utilization of hallway beds (aRR: 0.680; 95% CI [0.094, 1.267]), increased nursing overtime rate (aRR: 5.018; 95% CI [1.210, 8.826]), and not keeping the clean supply room door closed (aRR: -0.283; 95% CI [-0.536, -0.03]). CONCLUSIONS: The study confirmed the multifaceted nature of infection prevention and emphasized the importance of interdepartmental collaboration to improve patient safety.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".