Pivoting data and analytic capacity to support Ontario’s COVID-19 response
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Health care systems have faced unprecedented challenges due to the COVID-19 pandemic. Access to timely population-based data has been vital to informing public health policy and practice. Methods: We describe how ICES, an independent not-for-profit research and analytic institute in Ontario, Canada, pivoted existing research infrastructure and engaged health system stakeholders to provide near real-time population-based data and analytics to support Ontario's COVID-19 pandemic response. Results: reports on SARS-CoV-2 testing and COVID-19 vaccine coverage. These reports: 1) helped identify congregate care/shared living settings that needed testing and prevention efforts early in the pandemic; 2) provided early indications of inequities in testing and infection in marginalized neighbourhoods, including areas with higher proportions of immigrants and visible minorities; 3) identified areas with high test positivity, which helped Public Health Units target and evaluate prevention efforts; and 4) contributed to altering the province's COVID-19 vaccine roll-out strategy to target high-risk neighbourhoods and helping Public Health Units and community organizations plan local vaccination programs. In addition, ICES is a key component of the Ontario Health Data Platform, which provides scientists with data access to conduct COVID-19 research and analyses. Discussion and Conclusion: ICES was well-positioned to provide rapid analyses for decision-makers to respond to the evolving public health emergency, and continues to contribute to Ontario's pandemic response by providing timely, relevant reports to health system stakeholders and facilitating data access for externally-funded COVID-19 research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| 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