Cross Sectional Study of the Trend of Use of Medications and Complementary Therapy by Travelers during Flights
Bibliographic record
Abstract
Long distance travelling is associated with differential negative effects, collectively referred as ‘travel fatigue’, which results from anxiety about the journey. The study has aimed to determine the trends of using prescription drugs rather than over the counter medications and complementary alternative therapy taken by travelers. A quantitative research approach has been used and 629 travelers were recruited from Saudi Arabia. The survey questionnaire was developed by the researchers after a comprehensive literature review, and it has been modified based on a pilot study consisting of 45 people, in addition to the expert review. For a 4-month period, the survey was distributed electronically through social media, also to electronic surveys distributed to travelers at King Khalid International Airport. Travelers used either prescription and over the counter medications or Complementary alternative therapy or Non-pharmacological therapy (NPT). OTC medications such as products containing paracetamol and antihistamines were commonly used medications. About 54.6% of people reported that their choice was based on information gathered from other people’s experiences, such as friends and family, followed by pharmacists. Travelers believed that medications could be used without prescription, which confirmed the extensive need of educational training related to travel and medication use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".