Disparities in use of a virtual pediatric emergency department in Ottawa, Canada
Bibliographic record
Abstract
Objectives: Virtual care can facilitate access to pediatric emergency departments (EDs), but it is unclear if virtual care is equitably accessed by patients from marginalized communities. This study compares the use of a virtual pediatric ED between neighbourhoods with different levels of marginalization. Methods: This is a cross-sectional study of virtual ED visits per neighbourhood (defined by census dissemination area) within 100 km of a tertiary-care pediatric hospital in Ottawa, Ontario, from May to December 2020. Our primary outcome was incidence rate ratios (IRRs) of virtual ED visits for each quintile of the Ontario Marginalization Index's four dimensions: material deprivation, ethnic concentration, residential instability, and dependency. We conducted a negative binomial regression and adjusted for distance from the hospital. Results: There were 2920 virtual ED visits from 1076 dissemination areas. Compared to the first quintile of material deprivation (wealthier neighbourhoods), there were lower adjusted IRRs of virtual pediatric ED visits for the third (0.80, 95% confidence interval [CI] 0.68 to 0.94), fourth (0.79, 95% CI 0.67 to 0.94), and fifth (0.51, 95% CI 0.42 to 0.61) quintiles. The highest quintile of ethnic concentration (more diverse neighbourhoods) had a lower adjusted IRR compared to the lowest quintile (0.79, 95% CI 0.82 to 0.87). The adjusted IRR for the second quintile of residential instability was slightly higher than the first quintile (1.20; 95% CI 1.02 to 1.41). Adjusted IRR of visits did not vary by dependency. Conclusions: Wealthier and less ethnically diverse neighbourhoods displayed higher rates of virtual pediatric ED visits, after adjusting for distance to the hospital.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".