Characterizing the Use of Telepsychiatry for Patients with Opioid Use Disorder and Cooccurring Mental Health Disorders in Ontario, Canada
Bibliographic record
Abstract
Rural patients with opioid use disorder (OUD) face a variety of barriers when accessing opioid agonist therapy (OAT) and psychiatric services, due to the limited supply of physicians and the vast geographic area. The telemedicine allows for contact between patients and their physician—regardless of physical distance. Objective. We characterize the usage of telemedicine to deliver psychiatric services to patients with OUD in Ontario, as well as traits of treatment-seeking patients with opioid dependence and concurrent psychiatric disorders. Methodology. A retrospective cohort study was conducted using an administrative database for patients who received psychiatric services via telemedicine between 2008 and 2014 and who also had OUD. Results. We identified 9,077 patients with concurrent opioid use and other mental health disorders who had received psychiatric services via telemedicine from 2008 to 2014; 7,109 (78.3%) patients lived in Southern Ontario and 1,968 (21.7%) in Northern Ontario. Telemedicine was used more frequently to provide mental health services to patients residing in Northern Ontario than Southern Ontario. Conclusion. Telemedicine is increasingly being utilized throughout Ontario for delivering mental health treatment. There is an opportunity to increase access to psychiatric services for patients with opioid dependence and concurrent psychiatric disorders through the use of the telemedicine.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".