“I’m Going to Stop Myself Before Someone Stops Me”: Complicating Narratives of Volitional Substance Use Treatment
Bibliographic record
Abstract
Background: Often people assume that entry into drug treatment is a voluntary action for persons who use drugs (PWUD). This narrative informs the organizational and regulatory structure of most treatment programs and consequently affects patients’ ability to exert agency over their own treatment. Yet, this view ignores the complex interplay between individual and structural factors in peoples’ decision-making processes, particularly among people who use drugs who are stigmatized and criminalized. Treatment programs that assume voluntary entry may lack appropriate services for the populations of treatment seekers that they serve. Methods: This paper uses semi-structured interviews with 42 participants in Opioid Substitution Treatment (OST) (including patients, clinic doctors and staff, and advocates) informed by one of the author’s own lived experience in OST, to examine patients’ treatment decisions, and in particular, if and how, the structural context of drugs’ illegality/criminalization affected their willingness to pursue treatment. A Critical Discourse Analysis was used to identify key themes. Results: Interview data demonstrates that most people who use drugs enter treatment under constrained conditions related to drugs’ illegality. Themes that emerged included: 1. A feeling of limited choices due to drugs’ illegality; 2. Peer and family pressure; 3. Fear of losing children; and 4. Internalized stigma (i.e. feeling they are dirty or bad for using). Conclusion: Narratives that frame PWUD’s treatment decisions as volitional provide political cover to policies that criminalize PWUD by obscuring their effect on PWUD’s treatment decisions. Treatment models, particularly those that serve highly criminalized populations, should be re-conceptualized outside of normative narratives of individual choice, and be broadened to understand how larger structures constrain choices. By looking at macro-level factors, including the interplay of criminalization and drug treatment, programs can begin to understand the complexity of PWUD motivations to enter drug treatment. Recognizing the role of the War on Drugs as a force of oppression for people who use drugs, and that their treatment decisions are made within that setting, may enable people in treatment, and providers, to develop more productive ways of interacting with one another. Additionally, this may lead to better retention in treatment programs.
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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 | 0.000 | 0.000 |
| 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".