eConsults and Learning Between Primary Care Providers and Specialists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it