Travel Characteristics and Risk‐Taking Attitudes in Youths Traveling to Nonindustrialized Countries
Bibliographic record
Abstract
BACKGROUND: International travel to developing countries is increasing with rising levels of disposable income; this trend is seen in both adults and children. Risk-taking attitude is fundamental to research on the prevention of risky health behaviors, which can be an indicator of the likelihood of experiencing illness or injury during travel. The aim of this study is to investigate whether risk-taking attitudes of youths are associated with travel characteristics and likelihood of experiencing illness or injury while traveling to nonindustrialized countries. METHODS: Data were analyzed from the 2008 YouthStyles survey, an annual mail survey gathering demographics and health knowledge, attitudes, and practices of individuals from 9 through 18 years of age. Travelers were defined as respondents who reported traveling in the last 12 months to a destination other than the United States, Canada, Europe, Japan, Australia, or New Zealand. Risk-taking attitude was measured by using a four-item Brief Sensation-Seeking Scale. All p values ≤ 0.05 were considered significant. RESULTS: Of 1,704 respondents, 131 (7.7%) traveled in the last 12 months. Females and those with higher household income were more likely to travel (odds ratio = 1.6,1.1). Of those who traveled, 16.7% reported seeking pretravel medical care, with most visiting a family doctor for that care (84.0%). However, one-fifth of respondents reported illness and injury during travel; of these, 83.3% traveled with their parents. Males and older youths had higher mean sensation-seeking scores. Further, travelers had a higher mean sensation-seeking score than nontravelers. Those who did not seek pretravel medical care also had higher mean sensation-seeking scores (p = 0.1, not significant). CONCLUSIONS: Our results show an association between risk-taking attitudes and youth travel behavior. However, adult supervision during travel and parental directives prior to travel should be taken into consideration. Communication messages should emphasize the importance of pretravel advice, target parents of children who are traveling, and be communicated through family doctors.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".