Variation in the stringency of COVID-19 public health measures on self-reported health, stress, and overall wellbeing in Canada
Bibliographic record
Abstract
Evidence is building regarding the association between government implemented public health measures aimed at combating COVID-19 and their impacts on health. This study investigated the relationship between the stringency of public health measures implemented in Canada and self-reported mental health, physical health, stress, and wellbeing among a random sample of 6647 Canadians 18 years of age and older. The analysis was based on self-reported health data from the Canadian Perspectives on Environmental Noise Survey. This data was combined with the Oxford COVID-19 Government Response Tracker database, which included overall stringency index (SI), and four of its sub-components, i.e., school and business closures, restrictions on gatherings, and stay at home policies. Adjusted multivariate logistic regression models indicated that the magnitude of the overall SI was associated with higher or lower odds of reporting worse physical health, mental health, stress and/or overall wellbeing, depending on the measure evaluated. Similarly, policy directed at the four sub-components had varying impacts on the odds of reporting worse health, depending on the sub-component, the strength of the policy restriction, and the health outcome evaluated. The association between the strength of the public health measures and self-reported health, and how this may inform future policy, is discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".