Knowledge, Perceptions, and Preferred Information Sources Related to COVID-19 Among Central Pennsylvania Adults Early in the Pandemic: A Mixed Methods Cross-Sectional Survey
Bibliographic record
Abstract
PURPOSE: To explore public knowledge, understanding of public health recommendations, perceptions, and trust in information sources related to COVID-19. METHODS: A cross-sectional survey of central Pennsylvanian adults evaluated self-reported knowledge, and a convergent, mixed methods design was used to assess beliefs about recommendations, intended behaviors, perceptions, and concerns related to infectious disease risk, and trust of information sources. RESULTS: The survey was completed by 5,948 adults. The estimated probability of correct response for the basic knowledge score, weighted with confidence, was 0.79 (95% CI, 0.79-0.80). Knowledge was significantly higher in patients with higher education and nonminority race. While the majority of respondents reported that they believed following CDC recommendations would decrease the spread of COVID-19 in their community and intended to adhere to them, only 65.2% rated social isolation with the highest level of belief and adherence. The most trusted information source was federal public health websites (42.8%). Qualitative responses aligned with quantitative data and described concerns about illness, epidemiologic issues, economic and societal disruptions, and distrust of the executive branch's messaging. The survey was limited by a lack of minority representation, potential selection bias, and evolving COVID-19 information that may impact generalizability and interpretability. CONCLUSIONS: Knowledge about COVID-19 and intended adherence to behavioral recommendations were high. There was substantial distrust of the executive branch of the federal government, however, and concern about mixed messaging and information overload. These findings highlight the importance of consistent messaging from trusted sources that reaches diverse groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".