Clients’ experiences on North America’s first take-home injectable opioid agonist treatment (iOAT) program: a qualitative study
Bibliographic record
Abstract
BACKGROUND: To support public health measures during the COVID-19 pandemic, oral opioid agonist treatment (OAT) take-home doses were expanded in Western countries with positive results. Injectable OAT (iOAT) take-home doses were previously not an eligible option, and were made available for the first time in several sites to align with public health measures. Building upon these temporary risk-mitigating guidelines, a clinic in Vancouver, BC continued to offer two of a possible three daily doses of take-home injectable medications to eligible clients. The present study explores the processes through which take-home iOAT doses impacted clients' quality of life and continuity of care in real-life settings. METHODS: Three rounds of semi-structured qualitative interviews were conducted over a period of seventeen months beginning in July 2021 with eleven participants receiving iOAT take-home doses at a community clinic in Vancouver, British Columbia. Interviews followed a topic guide that evolved iteratively in response to emerging lines of inquiry. Interviews were recorded, transcribed, and then coded using NVivo 1.6 using an interpretive description approach. RESULTS: Participants reported that take-home doses granted them the freedom away from the clinic to have daily routines, form plans, and enjoy unstructured time. Participants appreciated the greater privacy, accessibility, and ability to engage in paid work. Furthermore, participants enjoyed greater autonomy to manage their medication and level of engagement with the clinic. These factors contributed to greater quality of life and continuity of care. Participants shared that their dose was too essential to divert and that they felt safe transporting and administering their medication off-site. In the future, all participants would like more accessible treatment such as access longer take-home prescriptions (e.g., one week), the ability to pick-up at different and convenient locations (e.g., community pharmacies), and a medication delivery service. CONCLUSIONS: Reducing the number of daily onsite injections from two or three to only one revealed the diversity of rich and nuanced needs that added flexibility and accessibility in iOAT can meet. Actions such as licencing diverse opioid medications/formulations, medication pick-up at community pharmacies, and a community of practice that supports clinical decisions are necessary to increase take-home iOAT accessibility.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".