Testing “the science”: A comparative analysis of COVID-19 testing policy across four Canadian provinces
Bibliographic record
Abstract
Following the COVID-19 pandemic, scholarship has focused on the intersection of politics and scientific evidence in the development, distribution and uptake of vaccines; border closures; and interventions such as public space closures or masking. But there is a significant gap in the examination of the political choices which informed how discrete jurisdictions chose to undertake and support COVID-19 testing. Using a qualitative, multiple-case study nested in a larger comparative, mixed-method explanatory case study, this research addresses this gap in the literature through a qualitative analysis based on 103 key stakeholder interviews to inform the narrative of testing strategy across four Canadian provinces. Despite the perception that testing is a largely “scientific” process relatively insulated from political choices and pressures, this study shows that jurisdictions had to address an array of variables, often specific to their region, which strongly influenced policy choices in this area. Testing policy, rather than a simple and straightforward clinical exercise, is a highly complex and nuanced process that must take into account a wide variety of non-clinical variables. • Non-scientific factors were important in implementing testing policy. • Testing policy differed across Canada because testing serves multiple purposes. • All provinces responded creatively to health human resource challenges. • Equity became a key consideration for all provinces in testing policy.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".