Experiences and Views of Domestic Summer Travelers During the COVID-19 Pandemic: Findings from a National Survey
Bibliographic record
Abstract
Domestic travel creates a serious risk of spreading COVID-19, including novel strains of the virus. Motivating potential travelers to take precautions is critical, especially for those at higher risk for severe illness. To provide an evidence base for communication efforts, we examined the experiences and views of travelers during the summer of 2020 through a telephone survey of 1,968 US adults, conducted in English and Spanish, July 2 through July 16, 2020. The survey found that more than one-quarter (28%) of adults had traveled domestically in the prior 30 days, most commonly for “vacation” (43%), and less than half wore masks (46%) or practiced social distancing (47%) “all of the time.” Although high-risk adults were significantly less likely to travel than non-high-risk adults (23% vs 31%; P < .001), they were no more likely to take precautions. Many travelers did not wear a mask or practice social distancing because they felt such actions were unnecessary (eg, they were outside or with friends and family). Although a substantial share of travelers (43% to 53%) trusted public health agencies “a great deal” for information about reducing risks while traveling, more travelers (73%) trusted their own healthcare providers. Findings suggest that outreach may be improved by partnering with providers to emphasize the benefits of layering precautions and provide targeted education to high-risk individuals. Messages that are empathetic to the need to reduce stress and convey how precautions can protect loved ones may be particularly resonant after more than a year of pandemic-related restrictions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".