Non-pharmaceutical interventions and vaccination during COVID-19 in Canada: Implications for COVID and non-COVID outcomes
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it