Proposed framework for a national set of reporting measures in Canada in response to the COVID-19 pandemic
Bibliographic record
Abstract
An effective strategy to control the ongoing coronavirus disease 2019 (COVID-19) pandemic takes into account inputs from many domains, including community epidemiology, surveillance and testing, contact tracing capacity, support for vulnerable populations, and health care system strain. Provincial and federal governments currently lack a universal approach to presenting relevant pandemic data from these domains to the general public in a way that engages them in decision making and promotes adherence to policies. We propose a framework to analyze COVID-19 pandemic data on an ongoing basis using inputs from these five domains, which can be scaled to the local public health unit, provincial, or national level. Data analysis was qualitative and semi-quantitative because there was a paucity of publicly available data on surveillance and testing, contact tracing, and health care system strain, which limited our ability to perform internal and external validation of our model. We urge the federal government to mandate a core set of reporting items across local, provincial, and federal jurisdictions that may then be used to perform validation and implementation of our proposed framework.
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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 | 0.034 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".