The potential use of digital technologies to enhance opioid agonist treatment in rural and remote communities of British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: People who use drugs (PWUD) and Indigenous-PWUD in rural and remote communities are met with considerable barriers in access to opioid agonist treatments (OAT) in comparison to their urban counterparts. OAT is met with low rates of uptake and retention owing to clinic policies limiting access to take-home doses. Digital technologies may improve access to OAT for PWUD in rural and remote communities. The objective of this study was to understand willingness to use an asynchronous witnessed-dosing phone application among rural and remote PWUD. METHODS: Qualitative semi-structured interviews were conducted with PWUD in rural and small communities in British Colombia, Canada (n = 32). Participants had to be ≥ 19 years old and have been on a prescribed OAT program within the 1-year of when the study began. A content analysis was completed on interview transcripts based on categories identified by the research team. RESULTS: Participants described access barriers to OAT medications, which was contingent upon housing-related factors. Regardless of living in town, in coastal communities, or on Tla'amin Nation treaty lands, participants described accessing OAT medications as restricting, impractical, and stigmatizing. When presented with a potential alternative delivery method via an asynchronous virtual application, participants described potential improvements in not only access to their OAT medications, but overall quality of life through, independence and increased opportunities to engage in day-to-day activities. Participants noted potential difficulties in keeping mobile devices. CONCLUSIONS: Our findings help illustrate how current OAT prescribing practices and the challenges with requiring in-person witnessed doses for people living in rural and remote communities, exercising control over OAT patients, ultimately limiting the autonomy of PWUD. There is a clear need to implement virtual OAT programs to improve access to OAT medications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".