Identifying as someone who avoids virus transmission strengthens physical distancing habit‐behaviour relationships: A longitudinal multi‐national study during the COVID‐19 pandemic
Bibliographic record
Abstract
Physical distancing remains an important initiative to curb COVID-19 and virus transmission more broadly. This exploratory study investigated how physical distancing behaviour changed during the COVID-19 pandemic and whether it was associated with identity with virus transmission avoidance and physical distancing habit strength. In a longitudinal, multinational study with fortnightly repeated-assessments, associations and moderation effects were considered for both overall (person-level means) and occasion-specific deviations in habit and identity. Participants (N = 586, M age = 42, 79% female) self-reported physical distancing behavioural frequency, physical distancing habit strength, and identity with avoiding virus transmission. Physical distancing followed a cubic trajectory, with initial high engagement decreasing rapidly before increasing again near study end. Physical distancing was associated with both overall and occasion-specific virus transmission avoidant identity and physical distancing habit strength. People with strong virus transmission avoidant identity engaged in physical distancing frequently regardless of fluctuations in habit strength. However, for those with weaker virus transmission avoidant identity, physical distancing was strongly aligned with fluctuations in habit strength. To enhance engagement in physical distancing, public health messaging might fruitfully target greater or more salient virus-transmission avoidance identity and stronger physical distancing habit.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".