Travel Medicine Research Priorities: Establishing an Evidence Base
Bibliographic record
Abstract
BACKGROUND: Travel medicine is the medical subspecialty which promotes healthy and safe travel. Numerous studies have been published that provide evidence for the practice of travel medicine, but gaps exist. METHODS: The Research Committee of the International Society of Travel Medicine (ISTM) established a Writing Group which reviewed the existing evidence base and identified an initial list of research priorities through an interactive process that included e-mails, phone calls, and smaller meetings. The list was presented to a broader group of travel medicine experts, then was presented and discussed at the Annual ISTM Meeting, and further revised by the Writing Group. Each research question was then subject to literature search to ensure that adequate research had not already been conducted. RESULTS: Twenty-five research priorities were identified and categorized as intended to inform pre-travel encounters, safety during travel, and post-travel management. CONCLUSION: We have described the research priorities that will help to expand the evidence base in travel medicine. This discussion of research priorities serves to highlight the commitment that the ISTM has in promoting quality travel-related research.
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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 | 0.488 | 0.627 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.049 | 0.029 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.041 | 0.045 |
| Open science | 0.011 | 0.018 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".