A Review of Gastrointestinal Outbreaks in Schools: Effective Infection Control Interventions
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to review documented outbreaks of gastrointestinal illness in schools, published in the last 10 years, to identify etiology, mode of transmission, the number of children affected, morbidity and mortality patterns, and interventions for control and prevention. METHODS: Searches of electronic databases, public health publications, and federal, state, and provincial public health Web sites were completed. RESULTS: Of the 121 outbreaks that met the inclusion criteria, 51% were bacterial, 40% viral, 7% were from Cryptosporidium, and 2% from multiple organisms. Transmission routes recorded in 101 reports included foodborne (45%), person-to-person (16%), waterborne (12%), and animal contact (11%). Actions to control outbreaks included alerting medical and public health authorities or the community to the outbreak (13%), treating cases (12%), enhancing hand washing (11%), and increased vigilance during food preparation (8%). Recommendations to prevent future outbreaks were compared with previously published studies that demonstrated effectiveness. CONCLUSIONS: The risk of foodborne illness was reduced when food handlers practiced effective hand washing technique and received food safety training and certification. Student training programs on hand hygiene, enhanced cleaning and disinfection of the school, and hepatitis A vaccination were found effective. Children should be supervised on farm visits, hand washing strictly enforced, and food eaten in an area separated from the animals. Staff and students should have a positive, continuous communication with public health authorities including educational sessions and immediate reporting of possible outbreaks.
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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".