Access to tablet injectable opioid agonist therapy in rural and smaller urban settings in British Columbia, Canada: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Rural and smaller urban settings in Canada are disproportionately impacted by the overdose crisis, highlighting the need for novel public health interventions within these jurisdictions. Tablet injectable opioid agonist therapy (TiOAT) programs have been implemented in select rural communities as a means to address drug-related harms. However, little is known about the accessibility of these novel programs. Therefore, we conducted this study to understand the rural context and factors that affected access of TiOAT programs. METHODS: Between October 2021 to April 2022, individual qualitative semi-structured interviews were conducted with 32 individuals enrolled in a TiOAT program at participating rural and smaller urban sites in British Columbia, Canada. Interview transcripts were coded using NVivo 12 and data were analyzed thematically. RESULTS: TiOAT access varied considerably. TiOAT delivery in rural settings is complicated due to geographic challenges. Participants who were homeless and staying at a nearby shelter or those in centrally-located supportive housing had minimal issues compared to those living in more affordable housing on the outskirts of town with limited transportation options. Dispensing policies that required daily-witnessed ingestion multiple times daily were challenging for most. Only one site provided evening take-home doses whereas participants at the other site could only resort to the illicit opioid supply to address withdrawal outside of program hours. Participants described the clinics as providing a positive and familial social environment compared to experiences of stigma elsewhere. Medication interruptions did occur when participants were in hospital and custodial settings, leading to withdrawal, program discontinuation, and overdose risk. CONCLUSIONS: This study highlights the beneficial ways in which health services tailored for people who use drugs can create a stigma-free environment with an emphasis on social bonds. Other factors such as transportation access, dispensing policies, and access in rural hospitals and custodial settings produced unique challenges for rural people who use drugs. Public health authorities in rural and smaller settings should consider these factors when designing, implementing, and scaling up future substance use services, including TiOAT programs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".