Changes in virus-transmission habits during the COVID-19 pandemic: a cross-national, repeated measures study
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic saw promotion of novel virus transmission-reduction behaviours, and discouragement of familiar transmission-conducive behaviours. Understanding changes in the automatic nature of such behaviours is important, because habitual behaviours may be more easily reactivated in future outbreaks and disrupting old habits may discontinue unwanted behaviours. DESIGN: = 42 ± 16y, 79% female). MAIN OUTCOME MEASURES: Within-participant habit trajectories across all timepoints, and engagement in transmission-reduction behaviours (handwashing when entering home; handwashing with soap for 20 seconds; physical distancing) and transmission-conducive behaviours (coughing/sneezing into hands; making physical contact) summed over the final two timepoints. RESULTS: Three habit trajectory types were observed. Habits that remained strong ('stable strong habit') and habits that strengthened ('habit formation') were most common for transmission-reduction behaviours. Erosion of initially strong habits ('habit degradation') was most common for transmission-conducive behaviours. Regression analyses showed 'habit formation' and 'stable strong habit' trajectories were associated with greater behavioural engagement at later timepoints. CONCLUSION: Participants typically maintained or formed transmission-reduction habits, which encouraged later performance, and degraded transmission-conducive habits, which decreased performance. Findings suggest COVID-19-preventive habits may be recoverable in future virus outbreaks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".