A Descriptive Comparison of Mass Testing During the COVID-19 Pandemic in Montreal, Paris, Bamako, and Recife
Bibliographic record
Abstract
Objective: The aim of this descriptive article was to compare mass testing for SARS-CoV-2 during the first wave of the COVID-19 pandemic in Montreal, Canada; Bamako, Mali; Paris, France; and Recife, Brazil. Methods: Data was collected through interviews with key informants involved in the testing response and a review of the grey literature. The TIDieR-PHP checklist was then used to provide the basis of the intervention descriptions and to compare the data between cities. Results: Descriptive comparisons revealed that the type of test, the testing process, and materials used were similar between the cities during the first wave of the pandemic. In addition, all cities experienced similar material and personnel resource shortages, directly affecting testing accessibility and capacity. The main differences were related to testing capacity and implementation timelines, which were dependent on the state of the health care systems, governance, and access to resources. Conclusion: Results of this study highlight the similarities and differences in testing between the cities and demonstrate the importance of comprehensive intervention descriptions to highlight lessons learned, increase knowledge sharing, and inform policy decisions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".