Driving Distance to Telemedicine Units in Northern Ontario as a Measure of Potential Access to Healthcare
Bibliographic record
Abstract
INTRODUCTION: The Ontario Telemedicine Network (OTN) uses technology to help make medical services more accessible to people in medically underserved rural and remote parts of Ontario, Canada. We examined access to OTN-enabled health and medical services in Northern Ontario, which has 775,000 people in communities scattered across an area of 803,000 km(2). MATERIALS AND METHODS: We used ArcGIS Network Analyst (Esri, Redlands, CA) to conduct a service area analysis with travel time as a measure of potential access to care. We used road distance and speed limits to estimate travel time between Northern Ontario communities and the nearest OTN unit. RESULTS: In 2014 there were 2,331 OTN units, of which 552 (24%) were located in Northern Ontario. All seven communities in Northern Ontario with a population of 10,000 or greater had OTN units. Almost 97% of the 59 communities with 1,000-10,000 people were within 30 min of an OTN unit. The percentage of communities within 30 min steadily decreased with decreasing population size, to 58% for communities with fewer than 50 people. In total, 86% (690/802) of Northern Ontario communities were within an hour's drive of an OTN unit. CONCLUSIONS: This study showed that most Northern Ontario communities were within an hour's drive of an OTN unit. The current distribution of OTN units has the potential to increase access to medical services and to reduce the need for medically related travel for residents of these communities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".