COVID-19 Experiences and Social Distancing: Insights From the Theory of Planned Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it