Enteric outbreaks in long-term care facilities and recommendations for prevention: a review
Bibliographic record
Abstract
Outbreaks of enteric illness in long-term care facilities (LTCFs) were reviewed to identify preventative recommendations. Systematic review methodology identified outbreak reports of gastrointestinal illness in LTCFs either published or that occurred from January 1997 to June 2007. The inclusion criteria captured 75 outbreaks; 23 (31%) associated with bacterial agents and 52 (69%) with viral agents. Transmission was mainly foodborne (52%) for those of bacterial origin and person-to-person (71%) for viral outbreaks. Norovirus infection was associated with 58% of hospitalizations. Sixty deaths were reported, about half from Salmonella infections. Recommendations for foodborne outbreaks emphasized appropriate sourcing and preparation of eggs, staff training, and temperature control during food preparation. Recommendations from outbreaks transmitted person-to-person centred on controlling residents' movements, effective environmental cleaning and disinfection, cancelling social events and restricting visitors, excluding ill staff, encouraging effective hand hygiene, and preventing cross-contamination through gloving and gowning. In none of the 75 published outbreak reports were the suggested recommendations evaluated for effectiveness in controlling the outbreak. Applied research of this type could greatly help in the acceptance of prevention and control strategies.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".