A mixed methods investigation of how young adults in Virginia received, evaluated, and responded to COVID-19 public health messaging
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".