Source of COVID-19 person-level data.
Bibliographic record
Abstract
<div> In Canada, lower income households and essential workers were disproportionately at risk of SARS-CoV-2. Early in the pandemic, stay-at-home restriction policies were used to limit virus transmission. There remains an evidence gap in how changes in mobility, in response to the policies, varied across socioeconomic measures in Canada. The study objective was to describe the variability in mobility change to two restrictions, by neighborhood-level income and by proportion essential workers across five regions in Ontario, Canada. The first restriction was implemented on March 17, 2020 in all five regions; and the second restriction was implemented in November 23, 2020 in two of the regions. Using cell-phone mobility data aggregated to the census tract, we compared the average mobility (% of devices that travelled outside their “primary location”) three weeks before and after each restriction. We defined the adjusted mobility change via pre-restriction mobility subtracted from post-restriction, adjusted for 2019 levels. We used difference-in-differences analysis to quantify effect modification of the second restriction’s effect by socioeconomic measures. With the first restriction, crude mobility fell from 77.7% to 41.6% across the five regions. The adjusted mobility change to the first restriction was largest in the highest-income neighborhoods (-43.3% versus -38.4%) and in neighborhoods with the fewest essential workers (-44.5% versus -37.6%). The overall adjusted mobility change to the second restriction was small: -0.96% (95% confidence intervals, -1.53 to -0.38%). However, there was evidence of effect modification by socioeconomic measures (less pronounced decrease in lower-income neighborhoods and more essential workers). The findings suggest a temporal saturation effect of restrictions over subsequent waves, and a saturation effect by income and occupation, leading to prevention gaps across populations by socioeconomic measures. Findings highlight the need for tailored approaches at the intersections of income and occupation when addressing epidemics of novel and resurging respiratory pathogens. </div>
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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".