eConsults and Learning Between Primary Care Providers and Specialists
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patients in many countries face poor access to specialist care. Electronic consultation (eConsult) improves access by allowing primary care providers (PCPs) and specialists to communicate electronically. As more countries adopt eConsult services, there has been growing interest in leveraging them as educational tools. Our study aimed to assess PCPs' perspectives on eConsult's ability to improve collegiality between providers and serve as an educational tool. METHODS: We conducted a qualitative content analysis of free-text comments left by PCPs using the Champlain BASE eConsult service based in Eastern Ontario, Canada. All responses provided between January 1, 2015 and January 31, 2017 that mentioned education or collegiality were included. RESULTS: PCPs completed 16,712 closeout surveys during the study period, of which 3,601 (22%) included free-text comments. Of these, 223 (6%) included references to education or collegiality. Three prominent themes emerged from the data: building provider relationships, teaching incorporated into answer, and prompting further learning. CONCLUSIONS: PCPs described eConsult's ability to foster stronger relationships with specialists, deliver responses that provided teaching in multiple areas of their practice, and support further learning that extended beyond the case at hand and into their overall practice. The Champlain BASE eConsult service has educational value for providers. Further study is underway to explore how questions and replies submitted through eConsult can be used to facilitate reflective learning and promote feedback to providers.
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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 | 0.024 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".