Attitudes, current behaviours and barriers to public health measures that reduce COVID-19 transmission: A qualitative study to inform public health messaging
Bibliographic record
Abstract
Public health measures to reduce COVID-19 transmission include masking in public places, physical distancing, staying home when ill, avoiding high-risk locations, using a contact tracing app, and being willing to take a COVID-19 vaccine. However, adoption of these measures varies greatly. We aimed to improve health messaging to increase adherence to public health behaviours to reduce COVID-19 transmission by: 1) determining attitudes towards public health measures and current behaviours; 2) identifying barriers to following public health measures; and, 3) identifying public health communication strategies. We recruited participants from a random panel of 3000 phone numbers across Alberta to fill a predetermined quota: age (18-29; 30-59; 60+ years), geographic location (urban; rural), and whether they had school-age children. Two researchers coded and themed all transcripts. We performed content analysis and in-depth thematic analysis. Nine focus groups were conducted with 2-8 participants/group in August-September, 2020. Several themes were identified: 1) importance of public health measures; 2) compliance with public health measures; 3) critiques of public health messaging; and 4) suggestions for improving public health messaging. Physical distancing and masking were seen as more important than using a contact tracing app. There were mixed views around willingness to take COVID-19 vaccine. Current public health messaging was perceived as conflicting. Participants felt that consistent messaging and using social media to reach younger people would be helpful. In conclusion, these findings provide insights that can be used to inform targeted (e.g., by age, current behaviour) public health communications to encourage behaviors that reduce COVID-19 transmission.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".