Risk Assessment in Travel Medicine: How to Obtain, Interpret, and Use Risk Data for Informing Pre‐Travel Advice
Bibliographic record
Abstract
BACKGROUND: It has been recommended that numerical risk data should be provided during the pre-travel consultation in order for travelers to make informed decisions regarding uptake of preventive interventions. METHODS: In this article, we review the definitions of the various risk measures, particularly as they relate to travel health, and discuss the study designs and methodological details required to obtain each measure. RESULTS: Risk measures can be broadly divided into absolute risk measures (including incidence rate, attack rate, and incidence density) and risk factor measures (including relative risk, risk ratio, and odds ratio). Although there are limitations inherent to each measure, absolute risk measures estimate the baseline risk for an "average" traveler, and risk factor measures help determine whether the risks for an individual traveler are likely to be higher or lower than this average, which is determined by specific traveler and itinerary characteristics. Incremental risk considerations add additional complexity, and risk communication plus risk perception/risk tolerance have additional impact on the individual traveler's interpretation of risk measures. CONCLUSIONS: Travel health practitioners should be aware of the complexities, limitations, and difficulties in understanding numerical risk data, as these factors are important in travelers' acceptance or rejection of interventions offered.
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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 | 0.025 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".