Challenges and recommendations for COVID-19 public health messaging: a Canada-wide qualitative study using virtual focus groups
Bibliographic record
Abstract
OBJECTIVES: To understand Canadian's attitudes and current behaviours towards COVID-19 public health measures (PHM), vaccination and current public health messaging, to provide recommendations for a public health intervention. DESIGN: Ten focus groups were conducted with 2-7 participants/group in December 2020. Focus groups were transcribed verbatim and analysed using content and inductive thematic analysis. The capability opportunity motivation behaviour Model was used as our conceptual framework. SETTING: Focus groups were conducted virtually across Canada. PARTICIPANTS: Participants were recruited from a pool of individuals who previously completed a Canada-wide survey conducted by our research team. MAIN OUTCOME MEASURE: Key barriers and facilitators towards COVID-19 PHM and vaccination, and recommendations for public health messaging. RESULTS: Several themes were identified (1) participants' desire to protect family and friends was the main facilitator for adhering to PHM, while the main barrier was inconsistent PHM messaging and (2) participants were optimistic that the vaccine offers a return to normal, however, worries of vaccine efficacy and effectiveness were the main concerns. Participants felt that current public health messaging is inconsistent, lacks transparency and suggested that messaging should include scientific data presented by a trustworthy source. CONCLUSIONS: ) openly address any unknowns, (3) more is better when sharing data, (4) use personalised stories to reinforce PHM and vaccinations, (5) humanise the message by calling out contradictions and (6) focus on the data and keep politics out.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".