Media Consumption and COVID-19–Related Precautionary Behaviors During the Early Pandemic: Survey Study of Older Adults
Bibliographic record
Abstract
BACKGROUND: During the COVID-19 pandemic, media sources dedicated significant time and resources to improve knowledge of COVID-19 precautionary behaviors (eg, wearing a mask). Many older adults report using the television, radio, print newspapers, or web-based sources to get information on political news, yet little is known about whether consuming news in the early phase of the pandemic led to behavior change, particularly in older adults. OBJECTIVE: The goals of this study were to determine (1) whether dosage of news consumption on the COVID-19 pandemic was associated with COVID-19 precautionary behaviors; (2) whether being an ever-user of social media was associated with engagement in COVID-19 precautionary behaviors; and (3) among social media users, whether change in social media use during the early stages of the pandemic was associated with engagement in COVID-19 precautionary behaviors. METHODS: Data were obtained from a University of Florida-administered study conducted in May and June of 2020. Linear regression models were used to assess the association between traditional news and social media use on COVID-19 precautionary behaviors (eg, mask wearing, hand washing, and social distancing behaviors). Analyses were adjusted for demographic characteristics, including age, sex, marital status, and education level. RESULTS: In a sample of 1082 older adults (mean age 73, IQR 68-78 years; 615/1082, 56.8% female), reporting 0 and <1 hour per day of media consumption, relative to >3 hours per day, was associated with lower engagement in COVID-19 precautionary behaviors in models adjusted for demographic characteristics (β=-2.00; P<.001 and β=-.41; P=.01, respectively). In addition, increasing social media use (relative to unchanged use) was associated with engagement in more COVID-19 precautionary behaviors (β=.70, P<.001). No associations were found between being an ever-user of social media and engaging in COVID-19 precautionary behaviors. CONCLUSIONS: The results demonstrated an association between higher media consumption and greater engagement in COVID-19 precautionary behaviors in older adults. These findings suggest that media can be effectively used as a public health tool for communication of prevention strategies and best practices during future health threats, even among populations who are historically less engaged in certain types of media.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".