456. Discrepant Trip Experiences Among Travelers Attending a Tertiary Care Center Family Travel Medicine Clinic
Bibliographic record
Abstract
International travel can expose travelers to a number of health risks. Pre-travel consultation helps prepare travelers for health concerns that might arise. The assessment of risk, mitigation strategies, and relevance of pre-travel advice is dependent on whether travelers adhere to their planned travel itinerary and activities. Objectives. We aimed to determine the proportion of returned travelers whose actual travel itineraries differed from their planned travel plans (defined as discrepant trip experiences). We also aimed to identify traveler or trip characteristics associated with discrepant trip experiences. We conducted a prospective cohort study at the Hospital for Sick Children’s Family Travel Medicine Clinic between September 2014 and December 2015. Pre- and post-trip questionnaires were compared with identify discrepant trip experiences. Among 186 participants, 121 (65%) reported their actual travel itineraries upon their return. A preliminary analysis of 53 participants revealed a median participant age of 37 years. Most common reasons for travel were vacation (n = 29, 55%) and visiting friends and/or relatives (n = 12, 23%). Median trip duration was 17 days (IQR 13 days); most commonly visited regions were Central America (n = 19, 36%), Asia (n = 18, 34%), and South America (n = 5, 9%). In total, 51 actual travel itineraries (96.2%, 95% CI 91–100) were discrepant from the pre-travel plans that were used to make pre-travel health recommendations. Additional activities (e.g., hiking, caving) (n = 42, 82.3%) and unplanned environments visited (e.g., altitude, jungle) (n = 32, 62.7%) during travel were the trip characteristics most likely to be discrepant. We did not identify any traveler demographic features or planned trip characteristics that predicted either discrepant trip experiences. Based on our preliminary analysis, the majority of travelers reported discrepant trip experiences. We plan to complete the analysis of the full cohort (N = 121) and also to quantify if the discrepant features meaningfully altered health risks during travel. This study informs practitioners providing pre-travel consultation to consider broader counseling as discrepancies from planned travel are common. All authors: No reported disclosures.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".