Improving access to specialist care in correctional facilities through Ontario eConsult
Bibliographic record
Abstract
OBJECTIVES: To evaluate the accessibility of multispecialty advice for primary care providers (PCPs) within correctional facilities, catering to the healthcare needs of individuals in federal custody in Ontario, Canada, through the utilization of electronic consultation (eConsult). DESIGN: Retrospective, cross-sectional, descriptive analysis. SETTING: eConsults submitted by PCPs within federal correctional facilities through the Ontario eConsult Service between April 1st, 2019, and March 31st, 2023. PARTICIPANTS: 906 completed eConsults were submitted by 21 PCPs in correctional facilities. RESULTS: The top three specialties sent to were cardiology (46%, N = 417), dermatology (14%, N = 128), and endocrinology and metabolism (8%, N = 68). The median specialist response time was 0.9 days. The median time specialists spent responding to each case was 15 minutes. PCPs received advice on a new or additional course of action in 34% of eConsult cases. In-person specialist appointments were avoided in 81% of cases. CONCLUSIONS: Ontario eConsult provides an ideal venue to improve access to multispecialty advice for people who are incarcerated. This service reduces the need for face-to-face specialist visits, decreases cost-of-care, and avoids unnecessary transportation outside of correctional facilities with potential security issues.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".