Prevention of respiratory outbreaks in the rehabilitation setting
Bibliographic record
Abstract
BACKGROUND: Respiratory viral (RV) outbreaks in rehabilitation facilities can jeopardise patient safety, interfere with patient rehabilitation goals and cause unit closures that impede patient flow in referring facilities. PROBLEM: Despite education about infection prevention practices, frequent RV outbreaks were declared each year at our rehabilitation facility. METHODS: Before and after study design. The primary outcome was the number of bed closure days due to outbreak per overall bed days. Process measures included delays in initiation of transmission-based precautions, RV testing and reporting of staff to occupational health and safety (OHS). Balancing measures included the number of isolation days and staff missed work hours. INTERVENTIONS: Based on comprehensive analysis of prior outbreaks, the following changes were implemented: (1) clear criteria for initiation of transmission-based precautions, (2) communication to visitors to avoid visitation if infectious symptoms were present, (3) exemption of staff absences if documented due to infectious illness, (4) development of an electronic programme providing guidance to staff about whether they should be excluded from work due to infectious illness. RESULTS: The number of bed closure days due to outbreak per overall bed days dropped from 2.8% to 0.5% during the intervention season and sustained at 0.6% during the postintervention season (p<0.001). There were fewer delays in initiation of droplet and contact precautions (28.8% to 15.5%, p=0.005) and collection of RV testing (42.9% to 20.3%, p<0.001), better reporting to OHS (9 vs 28.8 reports per 100 employees; p<0.001) and fewer isolation days (7.8% vs 7.3%; p=0.02) without a significant increase in missed work hours per 100 hours worked (4.0 vs 3.9; p=0.12). CONCLUSION: This Quality Improvement study highlights the process changes that can prevent respiratory outbreaks in the rehabilitation setting.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".