Identifying gaps in COVID-19 health equity data reporting in Canada using a scorecard approach
Bibliographic record
Abstract
Abstract Objective To assess thealth equity-oriented COVID-19 data reporting across Canadian provinces and territories, using a scorecard approach. Method A scan was performed of provincial and territorial reporting of five data elements (cumulative totals of tests, cases, hospitalizations, deaths and population size) across three units of aggregation (province or territory-level, health regions, and local areas) (15 “overall” indicators), and for two vulnerable settings (long term care and detention facilities) and six social markers (age, sex, immigration status, race/ethnicity, essential worker status, and income) (120 “equity-related” indicators). Per indicator, one point was awarded if case-delimited data were released, 0.7 points if only summary statistics were reported, and 0 if neither was provided. Results were presented using a scorecard approach. Results Overall, information on cases and deaths was more complete than for tests, hospitalizations and population size denominators needed for rate estimation. Information provided on jurisdictions and their regions, overall, tended to be more available (average score of 53%, “B”) than for equity-related indicators (average score of 21%, “D”). Only British Columbia and Alberta provided case-delimited data, and only Alberta provided information for local areas. No jurisdiction reported on outcomes according to patients’ individual-level immigration status, race, or income. Only Ontario and Quebec provided detailed information for long-term care settings and detention facilities. Conclusion Socially stratified reporting for COVID-19 outcomes is sparse in Canada. However, several “best practices” in health equity-oriented reporting were observed and set a relevant precedent for all jurisdictions to follow for this pandemic and future ones.
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How this classification was reachedexpand
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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".