COVID-19 Experiences and Social Distancing: Insights From the Theory of Planned Behavior
Bibliographic record
Abstract
PURPOSE: The objective of this study is to identify the relationship between COVID-19 experiences, perceived COVID-19 behavioral control, social norms and attitudes, and future intention to follow social distancing guidelines. DESIGN: This is a cross-sectional study. SETTING: Participants responded to an on-line survey in June 2020. SUBJECTS: The study included 3,183 residents within Quebec, Canada aged 18 and over. MEASURES: Measures include perceived COVID-19 related discrimination, fear of COVID-19 infection, prior exposure to COVID-19, and prior social distancing behavior. Participants self-reported attitudes, perceived behavioral control, and perceived norms related to social distancing. Finally, we measured social distancing behavioral intention. ANALYSIS: We evaluated a theory of planned behavior (TPB) measurement model of social distancing using confirmatory factor analysis (CFA). The association between COVID-19 perceived discrimination, fear of infection, previous social distancing behavior, exposure to COVID-19, TPB constructs and behavioral intentions to social distance were estimated using SEM path analysis. RESULTS: TPB constructs were positively associated with intention to follow social distancing guidelines. Fear of COVID-19 infection and prior social distancing behavior were positively associated with behavioral intentions. In contrast, perceived discrimination was negatively associated with the outcome. Associations between fear of COVID-19, perceived COVID-19 discrimination and behavioral intentions were partially mediated by constructs of TPB. CONCLUSIONS: COVID-19 prevention efforts designed to emphasize positive attitudes, perceived control, and social norms around social distancing should carefully balance campaigns that heighten fear of infection along with anti- discrimination messaging.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".