Perspectives of Champlain BASE Specialist Physicians: Their Motivation, Experiences and Recommendations for Providing eConsultations to Primary Care Providers
Bibliographic record
Abstract
Electronic consultation can improve access to specialist care. However, specialists have been identified as less likely to adopt electronic solutions in clinical settings. We conducted an online survey to explore the perspectives of specialists who use the Champlain BASE eConsult service in Eastern Ontario, Canada. Specialists were asked their opinions on experience with the service, their current consult/referral practices, recommendations for change and expansion of the service, and compensation models. We tabulated descriptive statistics from the multiple choice and Likert scale responses and performed a content analysis with an emergent code strategy for open-text responses. Specialists (n=34, 77% response rate) agreed that the Champlain BASE eConsult service is a feasible way to improve access to specialist care (94%), improves communication between specialists and primary care providers (PCPs) (94%), has educational value for PCPs (91%), and is user friendly (82%). A majority of specialists (88%) felt the service should be expanded provincially and 67% felt it should allow specialist-to-specialist consultation. 88% of specialists agreed that the current compensation process is best. This study provides an in-depth look at the perspective of the specialist physicians who use the Champlain BASE eConsult service. Specialists stated specific recommendations for change that will allow us to ensure the service remains sustainable.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".