A Population-based Comparison between Travelers Who Consulted Travel Clinics and Those Who Did Not
Bibliographic record
Abstract
BACKGROUND: Travel to hepatitis A-endemic countries is frequent among North Americans. Such travel carries significant risks for the individuals themselves and for the general population. We documented the patterns of use of travel clinics in a large Canadian adult population. METHODS: Travelers who had visited a hepatitis A-endemic country between 1990 and the time of the survey in 1999 were eligible. Subjects were identified from a representative sample of 4,002 adults from the two largest Canadian provinces. They were contacted by random digit dialing and interviewed by telephone. RESULTS: Only 15% of trips had been preceded by a visit to a travel clinic. The probability of visiting a travel clinic was approximately 10 times greater for travelers considered to be in the high-risk category than for those in the low-risk category, but the former represented only 2% of the total. The probability of visiting a travel clinic was approximately 23 times greater for travelers who were aware of the health risks in their country of destination. Income level was not associated with attendance at a travel clinic, and cost was rarely mentioned as a reason for not attending such a travel clinic before departure. CONCLUSIONS: Each year, millions of Canadian travelers go to hepatitis A-endemic countries without consulting a travel clinic. Active steps must be taken by public health authorities to improve their utilization of health services and prevent the accrued health risk for these travelers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".