US and Canadian Residents Are Equally Likely to Follow Public Health Recommendations: A Cross-National Mixed Methods Analysis
Bibliographic record
Abstract
Context: April 2020, at the beginning of the COVID-19 pandemic. Objective: Numerous factors shape population perceptions of public health guidelines. A better understanding of cross-cultural actions and attitudes about the recent pandemic may inform a more robust response to future public health crises. This study compares the knowledge of public health recommendations, perceptions, and trust in information sources related to COVID-19 between adults in the United States (US) and Canada (CA). Study Design: Convergent, mixed-methods global online survey. Data set: Online survey distributed via social media early in the COVID-19 pandemic. Population Studied: 180 adults from Canada and a 1:1 US cohort matched by age, gender, education status, socioeconomic status, and survey completion date (p=>.05 across all variables). Outcome Measures: Quantitative comparisons were made using Chi-squared and T-tests.. Conventional content analysis was used to analyze five open-ended questions with an intercoder kappa of ≥ 0.70 with constant comparison method to reconcile discrepancies. Convergent thematic assessment was used to analyze open ended responses which served to interpret quantitative similarities and differences between the two groups. Results: Quantitative analysis revealed significant differences. US survey respondents answered 73% of knowledge questions correctly, compared to 68% of CA respondents (p=<.001). CA significantly preferred different information sources (p=<.001). These differences were fleshed out in the qualitative analysis; the CA cohort expressed satisfaction with governmental response and news distribution, in stark contrast to the US group. There were no significant differences with intent to follow recommendations or belief in their effectiveness. In the qualitative analysis, The US cohort had themes of willingness to follow the guidelines but with a complementary desire for more access to autonomy in their choices. Conclusions: This study provides insight into population expressions of adherence and motives contributing to cross-cultural differences in health behavior motivations. A greater understanding of COVID-19 mitigation behaviors may improve the development of multicultural efforts to address future public health crises.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".