Specialist Participation in e-Consult and e-Referral Services: Best Practices
Bibliographic record
Abstract
Electronic consultations (eConsults) and referrals (eReferrals) are being implemented to improve access to specialist care. As eConsult and eReferral services rely on a roster of engaged specialists for their success, careful attention must be paid to how the term "specialist" is defined, what criteria inform specialists recruitment, and how quality of specialist responses can be monitored and maintained. Key considerations, informed by our personal experiences, review of best practice documents, international frameworks of specialists roles and competencies and a focused small group discussion among providers, health service planners and researchers for each of these important elements is discussed. Individuals participating in services should receive clear expectations around their role and responsibilities and be provided equitable access assuming they meet the necessary requirements. Training and feedback should be provided to ensure timely, quality responses. Paying attention to these key elements will reduce confusion, frustration and disengagement amongst specialists and ensure high quality responses.
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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 | 0.086 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".