Predictors of Pretravel Consultation in Tourists from Quebec (Canada)
Bibliographic record
Abstract
BACKGROUND: Although many tourists from Quebec (Canada) each year visit destinations at risk for infectious diseases, only a few of them seek travel health advice. To identify the determinants of travel health consultation, we conducted a study among Quebec's tourists visiting two popular sun destinations. METHODS: A conceptual model based on psychosocial determinants of human behavior was constructed. A cross-sectional survey was carried out, from January to April 1999, on two samples of travelers planning to visit Mexico and the Dominican Republic. One sample was composed of people who did not consult a travel clinic (cluster sampling in seven flights), and the other sample was one of clients of travel clinics (purposive selection of 13 specialized clinics located in Quebec). A 34-item self-administered bilingual questionnaire was distributed to travelers. Statistical analysis included a multivariate approach (logistic regression). RESULTS: A total of 2,242 travelers were surveyed (response rate in flight 75% and in clinics 99%). We present only results reported by French-speaking tourists: 1,152 who did not consult a travel clinic and were reached in flight, and 449 who were reached in clinics. Multivariate analyses indicated that travel agent recommendation was the most important predictor of consultation among travelers (OR 8.0, 95% CI 5.1-13), especially among those under 45 years of age and those who never sought pretravel consultation before (OR 21, 95% CI 11-41). Other important predictors were: traveling for the first time, traveling with children, previous consultation, perception about efficacy of immunization, risk perception, and information from travel agent, family doctor, and pharmacist. CONCLUSIONS: Despite its limitations, this study provides data that should help improve public health interventions aimed at encouraging travelers to get a pretravel consultation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".