Offering eConsult to Family Physicians With Patients on a Pain Clinic Wait List: An Outreach Exercise
Bibliographic record
Abstract
Wait times for many chronic pain programs in Canada range from 6 months to 2 years. This project sought to determine the interest of primary care providers (PCPs) in using an electronic consult system for patient(s) waiting for a pain consultation. This cross-sectional study was conducted at the pain clinic of a Canadian tertiary academic health sciences center. Participants were PCPs who had submitted a referral to this clinic. Referrals received between April 1, 2012, and March 31, 2014, were reviewed to determine their appropriateness for eConsult, and a letter providing information about eConsult and encouraging its use was sent to the referring PCP. Of the 585 referrals that were reviewed, 227 were appropriate for eConsult. Fifty-one (26%) of the 194 PCP responses received were positive. Technologies like eConsult may help address the growing demand for specialist advice. In addition to facilitating response to specific questions, the bidirectional nature of eConsult permits its use for educating PCPs about chronic pain treatment. Given that almost one third of responding PCPs indicated an interest in eConsult, its potential reach is vast. Additional study is needed to understand barriers to PCP acceptance and use of eConsult and the uptake of advice given.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".