Looking over the fence: how travel medicine can benefit from tourism research
Bibliographic record
Abstract
[Extract] After being a specialty for a quarter of a century, “travel medicine” is still very much about “medicine” with only limited consideration of pertinent “travel” or tourism aspects. Such aspects include a basic understanding of tourism theories and models, industry frameworks, and, most importantly, an insight into the traveling person who happens to be the declared focus of both medicine and tourism. Travelers do not travel within the context of medicine; they travel within the context of tourism. Before attending a travel clinic, the traveler has already been captured by a much larger structure in which he or she functions as a person with a distinct set of knowledge, attitudes, motivations, and perceptions relevant to the chosen trip. The same makeup influences also the behavior displayed from the point of leaving home to the return after the journey. The traveler participates in a global business, which has a keen interest in what this person wants and how the tourism product can best be delivered. For obvious reasons, this consumer of marketing, transport, accommodation, food, and activities is highly valued and studied by the tourism academia.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".