Accessible Care: The Human Rights Case for Take-Home Injectable Opioid Agonist Treatment
Bibliographic record
Abstract
Substance use disorders comprise a significant portion of disability experiences in Canada and are often experienced alongside other disabilities. During the pandemic, there were declines in healthcare and substance use treatment utilization. In response, new risk mitigation guidelines aimed to increase substance use treatment accessibility, advancing the right to available, accessible, acceptable, and quality healthcare. Injectable opioid agonist treatment (iOAT), a highly regimented treatment with daily supervised doses, became available in take-home doses for a select group of clients. We conducted qualitative interviews with iOAT clients to understand intersections of disability and accessibility with traditional and take-home iOAT. Twenty-three clients accessing either traditional iOAT, take-home iOAT, or delivery through COVID-19 mitigation guidelines were interviewed (2021-2022). Data were analyzed through a critical realist lens, using an abductive coding approach. Clients described traditional iOAT as, at times, inaccessible. Clients encountered barriers in neighbourhoods (e.g., sidewalk obstructions) and within social housing units (e.g., broken elevators), as well as through physical symptoms (e.g., chronic pain) and mental health concerns. Clinic attendance was impeded by drug- and treatment- related stigma and fear of discrimination (e.g., by employers, in social relationships). Conversely, take-home iOAT increased accessibility, offering clients a sense of freedom, dignity, autonomy, and free time, and thus greater emotional and social fulfillment. Take-home iOAT is a more accessible treatment approach that addresses the fluctuating needs of clients, especially those with additional disabilities. Expanding access to take-home iOAT aligns with human rights-based healthcare and uplifts economic, social, and cultural rights of people who need this care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".