Substance Use Recovery and Stigma in Rural Contexts
Bibliographic record
Abstract
BackgroundThe concept of recovery holds international legitimacy as a client-centered, strengths-based approach to mental health and substance use. How dominant recovery discourses shape the identities and affect the lives of people with substance use issues in rural areas have received little attention. The purpose of this three-paper dissertation is to better understand power operating within recovery discourses and trace the sociocultural processes by which stigmatized identities are constructed and resisted. Foucauldian and intersectionality theories are used to address the questions: 1) How do recovery discourses shape the identities of people with substance use issues in rural Ontario? 2) How do recovery discourses affect the daily lives of people with substance use issues in rural Ontario? 3) What strategies for resistance are employed to challenge substance use stigma? Methods Paper 1 presents a critical discourse analysis (CDA) of two federal documents to examine how recovery is understood in Canada. Paper 2 applies a CDA to 40 semi-structured interviews with people with substance use issues in two rural communities to explore how they adopt and contest recovery discourses. Paper 3 presents a thematic analysis of the same 40 interviews to consider how people with substance use issues experience recovery, stigma, and resistance. Findings Findings from Paper 1 illustrate that dominant recovery discourses are enmeshed in complex networks of neoliberal, biomedical, legal, and moral discourses that (re)produce stigmatized identities at macro and micro levels, complicating the recovery literature and challenging normative assumptions related to substance use. Papers 2 and 3 present the voices of people with substance use issues in rural areas. Their experiences and perspectives align with and contest dominant recovery discourses and provide numerous strategies of resistance to counter substance use stigma. Conclusion This thesis complicates the growing body of substance use recovery literature in rural communities. The analyses of federal documents and personal narratives not only expose underlying power relations and ideological values that construct a reductionist model of recovery and perpetuate stigmatized identities, but encourage researchers, educators, policymakers, practitioners, and marginalized groups to advocate for more equitable approaches to recovery that eradicate stigma related to rural substance use.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.024 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".