Travelers’ Knowledge of Prevention and Treatment of Travelers’ Diarrhea
Bibliographic record
Abstract
BACKGROUND: Information regarding the prevention and treatment of travelers' diarrhea (TD) is available to the public from various sources, such as medical personnel, travel clinics, personal contacts, and the Internet. This type of information may help travelers avoid this illness or help those afflicted minimize its duration. METHODS: We collected questionnaire data from 104 travelers at departure gates for flights to Mexico from Calgary, Alberta on their knowledge of symptoms and treatment of TD and food risks associated with this illness and sources of information used. RESULTS: Almost half reported they received some information on travel-related diseases and on TD prior to the flight. When education level was controlled for, the mean score for people who had obtained information on TD was significantly higher than that for those who did not have such information. College or university-educated travelers scored better than did other travelers. A high proportion of travelers correctly identified risk levels associated with specific foods consumed during travel, and many recognize that they are at an increased risk of acquiring diarrheal illness while traveling in a developing country. CONCLUSIONS: Information on TD appears to improve the level of knowledge on its prevention and treatment among travelers from southern Alberta.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".