Increasing Referral of At-Risk Travelers to Travel Health Clinics: Evaluation of a Health Promotion Intervention Targeted to Travel Agents
Bibliographic record
Abstract
BACKGROUND: Increases in travel-related illness require new partnerships to ensure travelers are prepared for health risks abroad. The travel agent is one such partner and efforts to encourage travel agents to refer at-risk travelers to travel health clinics may help in reducing travel-attributable morbidity. METHODS: A health promotion intervention encouraging travel agents to refer at-risk travelers to travel health clinics was evaluated. Information on the knowledge, attitudes, and behaviors of travel agents before and after the intervention was compared using two self-administered questionnaires. The Wilcoxon signed rank test was used to compare the mean difference in overall scores to evaluate the overall impact of the intervention and also subscores for each of the behavioral construct groupings (attitudes, barriers, intent, and subjective norms). Multiple regression techniques were used to evaluate which travel agent characteristics were independently associated with a stronger effect of the intervention. RESULTS: A small improvement in travel agents overall attitudes and beliefs (p =.03) was found, in particular their intention to refer (p =.01). Sixty-five percent of travel agents self-reported an increase in referral behavior; owners or managers of the agency were significantly more likely to do so than other travel agents (OR = 7.25; 95% CI: 1.64 32.06). Older travel agents, those that worked longer hours and those with some past referral experience, had significantly higher post-intervention scores. CONCLUSIONS: Travel agents can be willing partners in referral, and agencies should be encouraged to develop specific referral policies. Future research may be directed toward investigating the role of health education in certification curricula, the effectiveness of different types of health promotion interventions, including Internet-facilitated interventions, and the direct impact that such interventions would have on travelers attending travel health clinics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".