“They’re all struggling as well”: social and economic barriers and facilitators to self-managing chronic illness among marginalized people who use drugs
Bibliographic record
Abstract
PURPOSE: Self-management is recommended for addressing chronic conditions, and self-management programmes improve health behaviours and outcomes. However, social and economic factors have been neglected in self-management research, despite their relevance for marginalized groups. Thus, we aimed to explore barriers and facilitators that influence self-management among socioeconomically marginalized people who use drugs (PWUD). METHODS: Using community-based participatory methods, we developed a qualitative interview guide and conducted peer-led recruitment. Participants were admitted into the study after self-identifying as using non-prescribed drugs, having a chronic health issue, and experiencing socioeconomic marginalization. Data were analysed using reflexive thematic analysis, taking a relational autonomy lens. RESULTS: Participants highlighted substantial barriers to managing their health issues, mostly stemming from their social and economic environments, such as unstable housing, low income, lack of supportive social networks, and negative healthcare experiences. Participants also described how their ability to self-manage their chronic conditions benefited from specific aspects of social interactions, including close relationships, community connectedness, and engaging in peer support. CONCLUSIONS: Our findings suggest that structural interventions are needed to support self-management among marginalized PWUD, especially stable housing. Self-management supports for PWUD would benefit from including a range of low-barrier community-based options, peer work opportunities, and advocacy for needs.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".