Neuromodulation for opioid use disorder in Canada : risks, benefits, values, and justice
Bibliographic record
Abstract
Despite increased efforts to intervene via harm reduction, educational initiatives, and treatment, the overdose crisis continues to be a significant crisis in Canada. Neurotechnologies such as deep brain stimulation, focused ultrasound, and repetitive transcranial magnetic stimulation have gained traction as possible treatments for substance use disorders and shown promising preliminary results. However, neurotechnologies have been met with apprehension owing to fear, stigma, and reluctance to label addiction as a brain disorder. Further complicating this issue are sociocultural factors as marginalized communities are disproportionately burdened by opioid use disorder (OUD) while having unmet needs and a history of distrust in the health system. Taking a living ethics stance, this dissertation sought to examine the ethics of using neurotechnologies for OUD in Canada. Living ethics is a stance in empirical ethics which emphasizes ethical issues as lived and embodied by individuals and centers epistemic justice by advocating for collaboration with diverse interest-holders in moral deliberations of ethics research. This dissertation argues for the application of a living ethics stance in substance use bioethics research, outlining the key role people who use drugs (PWUD) can play in identifying and addressing ethical issues in substance use treatment. Using a living ethics stance, the empirical work of this dissertation included semi-structured interviews with PWUD (N=22) in Vancouver, BC. Data were analyzed using a narrative ethics approach to capture a holistic view of patient perspectives. Participants reported that foundational elements of their lives must be addressed alongside the development of novel treatments. This included fulfillment of basic health and social needs, as well as discrimination- and stigma-free health care that respects their autonomy. Surrounding neurotechnologies specifically, participants had concerns about the invasiveness, the inaccessibility of treatment protocols, the potential for further dehumanization of PWUD, and the experimental nature of neurotechnology. Despite these concerns, the majority favoured continued development, and many indicated interest in using neurotechnologies. This dissertation concludes with a discussion of practical implications and proposed recommendations to ensure neurotechnologies are developed to be accessible and equitable, and delivered in a stigma- and discrimination-free context where the basic needs of PWUD are fulfilled and patient autonomy is honoured.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".