Development of eConsult reflective learning tools for healthcare providers: a pragmatic mixed methods approach
Bibliographic record
Abstract
Abstract Background Electronic consultation (eConsult) programs are crucial components of modern healthcare that facilitate communication between primary care providers (PCPs) and specialists. eConsults between PCPs and specialists. They also provide a unique opportunity to use real-world patient scenarios for reflective learning as part of professional development. However, tools that guide and document learning from eConsults are limited. The purpose of this study was to develop and pilot two eConsult reflective learning tools (RLTs), one for PCPs and one for specialists, for those participating in eConsults. Methods We performed a four-phase pragmatic mixed methods study recruiting PCPs and specialists from two public health systems located in two countries: eConsult BASE in Canada and San Francisco Health Network eConsult in the United States. In phase 1, subject matter experts developed preliminary RLTs for PCPs and specialists. During phase 2, a Delphi survey among 20 PCPs and 16 specialists led to consensus on items for each RLT. In phase 3, we conducted cognitive interviews with three PCPs and five specialists as they applied the RLTs on previously completed consults. In phase 4, we piloted the RLTs with eConsult users. Results The RLTs were perceived to elicit critical reflection among participants regarding their knowledge and practice habits and could be used for quality improvement and continuing professional development. Conclusion PCPs and specialists alike perceived that eConsult systems provided opportunities for self-directed learning wherein they were motivated to investigate topics further through the course of eConsult exchanges. We recommend the RLTs be subject to further evaluation through implementation studies at other sites.
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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 | 0.242 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".