Attitudes towards Avian influenza and sources of media information in travelers to developing countries
Bibliographic record
Abstract
Background: Although there is an on-going 2009 H1N1 influenza epidemic, avian influenza virus (A/H5N1) continues to be a significant public health threat. Currently, 442 cases have been confirmed worldwide with 262 deaths, mostly in Asian countries. Risk of disease may be higher in travelers to developing destinations, where these cases occur more frequently. This study investigated travelers to developing countries (TDC) and described their attitudes towards A/H5N1 and defined their sources of media information in order to inform focused avian influenza prevention campaigns for travelers. Methods: Data were analyzed from the 2008 Porter-Novelli ConsumerStyles survey, an annual national mail-in survey that gathers demographic information and media/consumer information about the US population. TDC were defined as persons traveling outside the United States for ≥1 day anywhere other than Canada, Europe, Japan, Australia, or New Zealand. Odds ratios (OR) and logistic regression were used. Results: Of 10,108 respondents, 913 (9%) reported being TDC; compared to non-TDC, TDC were less likely to be worried about getting ill from A/H5N1 (OR = 0.5, CI = 0.4-0.8, p = 0.002). Further, TDC were less likely to have followed news stories about A/H5N1 (OR = 0.72, CI = 0.56-0.95, p = 0.02) and were more likely to feel that news media were “exaggerating the dangers” (OR = 1.3, CI = 1.1-1.5, p = 0.006), compared to feeling the “news reports are about right.” Overall, TDC were more likely to refer to the Internet (OR = 1.5, CI = 1.3-1.7, p < 0.0001) for health information than were non-TDC. They were also more likely to read the national news (OR = 1.3, CI = 1.2-1.5, p < 0.0001) or travel sections (OR = 3.0, CI = 2.6-3.4, p < 0.0001) of the newspaper. TDC were more likely to view travel programs on television (OR = 1.6, CI = 1.4-1.9, p < 0.0001). However, for both newspaper and television, the two groups did not differ significantly in reading the health section or watching health shows. Conclusion: Given the initial spread of the 2009 H1N1 virus through travelers and the ongoing threat of A/H5N1, it is important to tailor health messages carefully to best communicate the importance of avian influenza risk to travelers. TDC will likely be better reached via information on the Internet and travel-related media sources. Abstracts for SupplementInternational Journal of Infectious DiseasesVol. 14Preview Full-Text PDF Open Archive
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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 | 0.004 |
| 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) | 0.003 | 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".