Compliance to preventive measures during the first wave of the COVID-19 pandemic in Canada: a joint trajectory analysis
Bibliographic record
Abstract
Research related to COVID-19 saw an exponential growth following the World Health Organisation’s announcement on the severity of the virus outbreak, classifying it a pandemic. Researchers from diverse fields were mandated by government agencies to answer society’s pressing questions in response to the COVID-19 pandemic, whether they were related to the virus’ transmission modes or to the communities adaptive capacities. One of the more prominent themes in the COVID-19 literature is compliance to preventive measures, namely hand washing, mask wearing, social distancing, and staying at home. Early on, during the first wave of the pandemic, studies reported that a small minority did not comply with governmental guidelines, furthering the need to explore reasons behind this transgressive behaviour. These studies, mostly correlational at first and longitudinal retrospectively, evaluated compliance levels to preventive measures as a fixed phenomenon without considering transition movements between levels of compliance. Furthermore, several social and sociodemographic factors were identified as probable reasons behind non-compliance to preventive measures, but these factors were only examined in regards to their relationship with compliance levels and not change in compliance levels. Hence, the present study aims to identify and describe Canadians’ compliance behaviours, whether they are stable or varying, and determine the factors’ association with transition movements of compliance levels.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".