Unveiling the association between information sources and young adults' attitudes and concerns during COVID-19: Results from the iCARE study
Bibliographic record
Abstract
OBJECTIVES: Throughout COVID-19, uncertain information on the virus and preventive measures circulated. Young adults, often relying on social rather than traditional media, showed lower adherence to recommendations. This study examines associations between information sources, attitudes toward public health measures and concerns among young adults. STUDY DESIGN: A repeated cross-sectional design was employed. METHODS: We analyzed a sample of 2121 Canadians aged 18-29 from the iCARE study. Participants were recruited via a polling firm's web panel between October 2020 and June 2021. RESULTS: Regression analyses showed that those extensively consulting traditional media (OR = 1.9, 95 % CI:1.4-2.4) and internet news (OR = 2.1, 95 % CI:1.6-2.7) were more likely to report that implementing preventive measures was important. Those consulting traditional media were less likely to report their strictness (OR = 0.6, 95 % CI:0.4-0.8). Extensive social media use was unrelated to these variables (ps > 0.60). Consulting extensively traditional media was associated with higher health (β = 0.18, p < 0.001) and social (β = 0.10, p = 0.02) concerns; internet news with greater health (β = 0.25, p < 0.001) and social (β = 0.04, p < 0.001) concerns; social media only with social concerns (β = 0.13, p = 0.008). Financial concerns were not associated with any information source (ps > 0.11). CONCLUSION: Heavy reliance on traditional media and internet news was associated with greater concerns and positive attitudes toward preventive measures. Heavy reliance on social media was not associated with positive attitudes but with social concerns. Findings underscore the complex link between media behaviour and individual perceptions, stressing the need for governments to acknowledge this issue to promote positive attitudes and reduce concerns in future public health crises.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".