REACHING YOUTH WITH RELIABLE INFORMATION DURING THE COVID-19 PANDEMIC: “SOCIAL MEDIA FOR SURE”
Bibliographic record
Abstract
As the COVID-19 pandemic evolves, it is important to continue providing accurate updates and public health information to various target audiences. In support of such efforts, this study aims to understand how youth have accessed information about COVID-19 and to record their perspectives on how such information is best communicated. As part of a larger longitudinal study, 463 youth (M = 21.2 years, SD = 2.2) were surveyed about their sources of information on COVID-19, with qualitative questions regarding their perspectives on optimal public health communication strategies. A majority of youth reported using online sources to access information about COVID-19, including online news sources and social media. They used a diversity of such sources, with a preference those they regarded as reliable. Participants recommended that public health information campaigns be conducted on a variety of social media channels. Other digital campaigns were also recommended, while some suggested providing information through schools. Information should be brief, engaging, accessible, and frequently updated, using verified sources to ensure accuracy. We conclude that, to reach youth effectively, it is essential that accurate COVID-19 information and public health guidelines be disseminated in an engaging manner using digital means, particularly social media. Communication campaigns should be developed in partnership with youth in order to best reach this audience with the information they need.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".