Spatial variations of COVID-19 risk by age in Toronto, Canada
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
The risk of coronavirus disease 2019 (COVID-19) may vary by age, biological, socioeconomic, behavioural and logistical reasons may be attributed to these variations. In Toronto, Canada, the aging population has been severely impacted, accounting for 92% of all COVID-19 deaths. Four age groups: 60-69 years, 70-79 years, 80-89 years and ≥90 years in Toronto neighbourhoods were investigated for clustering tendencies using space-time statistics. Cohen's Kappa coefficient was computed to assess variations in risk by neighbourhood between different age groups. The findings suggest that knowledge of health risks and health behaviour varied by age across neighbourhoods in Toronto. Therefore, understanding the socioecological context of the communities and targeting age-appropriate intervention strategies is important for planning an effective mechanism for controlling the disease.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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