The Appraisal and Endorsement of Individual and Public Preventive Measures to Combat COVID-19 and the Associated Psychological Predictors among Chinese Living in Canada
Bibliographic record
Abstract
Aims: The study examines the factors related to the appraisal and adherence of the individual and public health preventive measures. Background: The effectiveness of the measures battling the pandemic was largely determined by the voluntary compliance of the public. Objectives: This study aimed to identify psychological perception factors related to the appraisal of individual measures and endorsement of public health measures during the early stage of the COVID-19 pandemic among Chinese living in Canada. Methods: A convenience sample of 656 participants completed an online survey. Nonparametric Kruskal Wallis tests were used to compare COVID perception variables ( e.g ., perceived susceptibility, fear, perceived severity, and information confusion) among different sociodemographic subgroups. Bootstrapped regression models were used to assess the association of these variables with outcome measures. Results: Compared to their counterpart groups, lower perceived susceptibility was reported by adults 65 years and older ( p = .002) or retired ( p = .015); greater fear was reported by females ( p = .044), those with lower education ( p = .001), and Mainland Chinese ( p = .033); greater perceived severity was reported by individuals with lower education and smaller household size ( p s = .003). Perceived susceptibility was inversely associated with individual measure appraisal ( p = .032). Perceived severity was positively associated with individual measure appraisal ( p = .005) and public measure endorsement ( p < .001). Conclusion: Individual behaviour measure appraisal was predicted by lower perceived susceptibility and higher perceived severity, whereas public health measure endorsement was related to higher perceived severity. These results inform the public and the policymakers about the critical factors that affect the preventive measure appraisal and endorsement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".