Norms and COVID‐19 health behaviours: A longitudinal investigation of group factors
Bibliographic record
Abstract
Abstract Most studies on norms and COVID‐19 have ignored the group‐based and dynamic nature of normative influence where self‐relevant and salient groups might emerge and change along with their impact on health behaviours. The current research seeks to explore these issues using a three‐wave longitudinal design with a representative sample of Australians ( N wave 1 = 3024) where two group sources of potential normative influence (neighbourhood and national groups) and two COVID‐19 health behaviours (physical distancing and hand hygiene) were investigated in May, June/July and September/October 2020. Results indicated that especially from Wave 1 to Wave 2 neighbourhood descriptive norms (rather than national or injunctive norms) had the most impact on health behaviours while controlling for demographic and individual‐level health variables. This demonstrates that groups and associated norms that influence behaviours vary across time. It is concluded that research on norms needs to study which groups matter and when.
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 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 | 0.003 | 0.000 |
| 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 | 0.001 |
| 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 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".