Specialist Perspectives on Ontario Provincial Electronic Consultation Services
Bibliographic record
Abstract
BACKGROUND: Wait times to access specialist care remain a huge frustration for patients and providers. In Ontario, two electronic consultation (eConsult) services provide prompt, secure access to specialist advice: The Champlain Building Access to Specialists through eConsultation (BASE™) eConsult-managed service, and the Ontario Telemedicine Network (OTN). INTRODUCTION: To gain a broader understanding of specialists' perspectives providing eConsult services, we surveyed all specialists actively participating in either platform. METHODS: A 34-item web questionnaire focused in four key areas (experience with the service, ideas for provincial expansion, recommendations for enhancements to the service, and specialist demographics) was sent to all specialists who had completed at least one eConsult on either service. RESULTS: There was a 66% (114/172) response rate for BASE and a 47% (61/130) response rate for OTN. The most frequent motivations for participating in eConsult were innovative patient care (58% and 69%), opportunity to reduce wait times (45% and 54%), and opportunity to communicate directly with primary care providers (41% and 51%). Most specialists agreed that eConsult is feasible, results in improved communication between providers, and can be integrated into their clinical workflow without difficulty. Fifty-two percent of OTN specialists and 49% of BASE specialists agreed that they were appropriately compensated for answering eConsults. DISCUSSION: Specialists participate in eConsult services to improve communication with primary care, provide innovative care, and reduce wait times. CONCLUSIONS: As eConsult services expand across regions and provinces, the provider perspectives and experiences should be used to evaluate the benefits of eConsult and impact on provider satisfaction.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".