Assessment of Equitable Use of Virtual Care in Pediatric Specialized Care
Bibliographic record
Abstract
Objective : Telemedicine and broadly, virtual care are established modes of health care delivery but may present unintended barriers to access. We assessed postpandemic ambulatory care provision to determine whether marginalized areas were underrepresented among virtual visits. Methods : We retrospectively analyzed 396,624 ambulatory visits (January 2022 through December 2023), using patient demographics and the Ontario Marginalization Index to compare virtual (78% electronic health record-integrated video) and in-person visits across the domains of residential instability, material deprivation, dependency, and ethnic concentration. Logistic regression was used to compare virtual to in-person visits, adjusting for age, sex, and geographic location. Results : We found higher virtual care utilization for children in remote areas (41% [OR: 1.72 {1.66–1.78}]) and progressively higher virtual care utilization across age groups. Virtual care use for children aged 1–12 was 27% (OR: 1.57 [1.53–1.62]); for adolescents aged 12–16 was 33% (OR: 2.02 [1.96–2.09]); and for those over 16 years was 38% (OR: 2.56 [2.49–2.64]), compared to infants (19%). Indices of residential instability, dependency, and material deprivation had minimal impact on access to virtual care; however, areas with high ethnic concentrations had significantly fewer virtual visits compared to the least ethnically concentrated areas (26.1% vs. 35.0%; adjusted OR: 0.65 [0.63–0.67]). Each quintile increase in marginalization within the ethnic concentration index was associated with an 11% decrease in the odds of a virtual visit. Conclusions : Virtual care use was higher for those at greater distance but lower in ethnically concentrated areas. Further investigation of strategies targeting language barriers, technological literacy, and cultural beliefs is warranted.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".