Co-Constructing a Community-Based Telemedicine Program for People With Opioid Use Disorder During the COVID-19 Pandemic: Lessons Learned and Implications for Future Service Delivery
Bibliographic record
Abstract
The COVID-19 pandemic triggered unprecedented expansion of telemedicine, including in the delivery of opioid agonist treatment (OAT) for people with opioid use disorder (OUD). However, many people with OUD lack the technological resources necessary for remote care, have complex needs, and are underserved, with precarious access to mainstream services. To address the needs of these individuals, we devised a unique program to deliver OAT via telemedicine with the support of community outreach workers in Montreal (Quebec, Canada). The program was co-constructed by the service de médecine des toxicomanies of the Centre hospitalier de l'Université de Montréal (CHUM-SMT)-a hospital-based addiction medicine service-and CACTUS Montréal-a community-based harm reduction organization known and trusted by its clientele. All procedures were jointly developed to enable flexible and rapid appointment scheduling. CACTUS Montréal workers promoted the program, facilitated private on-site telemedicine connections to the CHUM-SMT, accompanied patients during web-based appointments if requested, and provided ongoing holistic support and follow-up. The CHUM-SMT offered individualized OAT regimens and other health services as needed. Overall, our experience as clinicians and community-based workers intimately involved in establishing and running this initiative suggests that participants found it to be convenient, nonjudgmental, and responsive to their needs, and that the implication of CACTUS Montréal was highly valued and integral to patient engagement and retention. Beyond the context of the COVID-19 pandemic, similar programs may present a flexible and accessible means to deliver alternative treatment options for people with OUD disengaged from traditional care, bridge gaps between communities and health providers, and improve access to care in rural or remote settings.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".