Non-pharmaceutical interventions and vaccination during COVID-19 in Canada: Implications for COVID and non-COVID outcomes
Bibliographic record
Abstract
Background As a federal country where health prerogatives are primarily at the subnational level (provinces), Canada has implemented non-pharmaceutical interventions (NPIs) of differing stringency and attained varied COVID-19 vaccination coverage across the different vaccination campaigns. NPIs and vaccination may have thus interacted in different ways. Methods A mixed-methods design combining a regression analysis and a comparative case study. The regression analysis focuses on COVID-19 outcomes such as COVID-19 cases, deaths, hospitalizations, and admissions in intensive care units. The case study centers on three provinces and explores outcomes beyond COVID-19, such as spillover on the healthcare system and the economy. Results While more stringent NPIs are associated with lower COVID outcomes, their interaction with vaccination coverage depends on the vaccination campaign. Increasing the vaccination coverage with more stringent NPIs was not associated with a decrease in COVID cases growth rate during the primary campaign (two-doses), however it was associated with a decrease in COVID hospitalizations during the booster campaign. For non-COVID outcomes, having less stringent restrictions and lower initial vaccination coverage did not help prevent longer wait times for healthcare nor higher initial unemployment. Conclusion The differing interaction between NPIs and vaccination coverage suggests that the interaction was more effective when the vaccine uptake was primarily from high-risk populations. Confirming this finding would require further detailed microdata analysis.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".