Examining Differences in Utilization of the Ontario eConsult Service in Rural Versus Urban Settings: A Retrospective Cross-Sectional Analysis
Bibliographic record
Abstract
INTRODUCTION: We conducted a retrospective, cross-sectional analysis exploring patterns of usage and outcomes from urban vs. rural eConsults to examine eConsult's impact on equity of access in rural Ontario, Canada. Patients living in rural regions face many barriers in accessing specialist care. The Ontario eConsult Service connects primary care providers (PCP) with specialists regardless of geographical location, improving equity of access. METHODS: We included all Ontario eConsult cases submitted between January 1 and December 31, 2021. Usage data collected automatically by the service and responses to a mandatory closeout survey were analyzed using descriptive statistics. Cases were identified as rural using the forward sorting area of the PCP's primary practice. RESULTS: Of the 72,948 cases submitted during the study period, 7550 were coded rural. Usage among rural PCPs was most frequent in Ontario Health North East (1.78 eConsult cases/1000 residents) and Ontario Health North West (1.64). Rural and urban eConsult cases had the same top 5 most frequently requested specialties. Both groups had median response times of 1.0 days, reported time billed of 15 min, and cost per case of $50. CONCLUSIONS: PCPs in rural and urban regions use eConsult with equal frequency and had similar usage patterns and outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".