Feasibility of Obtaining Sufficient Numbers of Responses to Questions About Travel Intentions, Thereby Facilitating Effective Health Messaging
Bibliographic record
Abstract
Introduction: The medical literature has identified a variety of health risks associated with travel. Risks depend on the susceptibility of the traveler, the specifics of the destination, the mode of transport, and on chance events. Ill-prepared travelers who underestimate travel risks may encounter a variety of health problems. In order to eventually increase the capability of travel risk prediction, the current study aimed to ascertain travel intent in China, a country traditionally difficult to penetrate through online survey. Methods: This pilot survey study used a reliable, anonymous, online survey method to determine the feasibility of obtaining a sufficient response in China to enable travel risk prediction. Results: The results are encouraging in that seven and a half thousand individuals in China responded over the course of one month. Most responders were from urban centers. Three to eleven percent of the respondents were over age 55 and planning to travel to potentially hazardous destinations. Conclusion: The combination of older age and geographic risk increases the chance of ill health during travel. Knowing who is planning to travel, where they are from, and where, when, and how they are planning to arrive at their destination opens a corridor to effective preventive public health programming and educational initiatives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.068 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".