Developing a Theoretically Informed Implementation Model for Telemedicine-Delivered Medication for Opioid Use Disorder: Qualitative Study With Key Informants
Bibliographic record
Abstract
BACKGROUND: Telemedicine-delivered medication for opioid use disorder (TMOUD) has become more prevalent during the COVID-19 pandemic, particularly in North America. This is considered a positive development as TMOUD has the potential to increase access to evidence-based treatment for a population heavily affected by the opioid crisis and consequent rising mortality and morbidity rates in relation to opioid use disorder. Despite the increase in the use of TMOUD, there are no established service- and process-focused models to guide the implementation of this intervention. OBJECTIVE: This study aims to develop a process- and service-focused implementation model in collaboration with key stakeholders and bring together peer-reviewed literature, practice-based knowledge, and expert opinions. METHODS: The simple rules for evidence translation in complex systems framework was applied to guide the development of a 6-step qualitative study. The steps were definition of the scope and objectives of the model, identification of evidence, stakeholder engagement, draft model development, key informant consultation, and final model specification. RESULTS: The final specification for the TMOUD implementation model incorporated key strategic priorities, service delivery prerequisites, service design elements, stakeholder identification and engagement, key process domains, and iterative cycles of evaluation and improvement. CONCLUSIONS: Through stakeholder engagement and key informant consultation, we produced a process- and service-focused TMOUD implementation model. The model is modifiable to different contexts and settings while also in keeping with the current evidence base and national and international standards of high-quality opioid use disorder care.
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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 | 0.078 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".