Virginia Journal of Public Health
Bibliographic record
Abstract
The purpose of this study was to investigate how young adults in Virginia received, evaluated, and responded to messages related to the coronavirus/COVID-19, a major disruptor of our time, and to understand how and when these messages influenced behavior. This was a sequential explanatory mixed methods study, including an online survey (quantitative) and virtual focus groups (qualitative). We surveyed a convenience sample of 3,694 Virginia residents by distributing a link to complete the survey online. Only data from18-24 year old adults (n=207) were included in the analysis for this study. Focus group participants were recruited from the survey participants as well as from a college-level introductory health class. Most (83%) young adult respondents reported national science and health organizations as a trusted source for COVID-19 information and over 50% of respondents reported getting information from state/local health departments (72%), healthcare professionals (71%), and online news sources (51%). Focus group participants emphasized social media as an additional major source of COVID-19 information. Focus group data revealed that young adults struggled with deciphering contradictory messaging, had a mix of logical and emotional reasons for deciding whether to adhere to guidelines, had a desire for consistent, fact-based public health messaging at the national level. The findings from this study underscore the importance of consistent, positive public health messaging in a public health crisis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.144 | 0.034 |
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".