Untangling the Complexities of Substance Use Initiation and Recovery: Client Reflections on Opioid Use Prevention and Recovery From a Social-Ecological Perspective
Bibliographic record
Abstract
BACKGROUND: In Canada, the rate of opioid use, opioid use disorder (OUD), and associated mortality and morbidity are higher among Indigenous Peoples than the general population. Indigenous Peoples on medications for opioid use disorders (MOUD) often face distinct barriers that hinder their clinical progress, leading to treatment attrition. METHODS: We used a social-ecological model to inquire into clients' experiences with a history of treatment failure for OUD. We used exploratory qualitative research to engage 22 clients with a history of OUD treatment dropouts and who are currently on MOUD. In-depth, semi-structured interviews lasting an average of 30 minutes were conducted on-site. RESULTS: We identified 4 themes from the study: (a) risk for substance use; (b) factors sustaining substance use; (c) factors leading to treatment, and (d) treatment failure and re-enrollment. CONCLUSION: Using a socio-ecological model helps to understand factors that influence an individual's risk for OUD, decision to pursue treatment, and treatment outcomes. Furthermore, social ecological model also creates possibilities to develop supportive, multilevel interventions to prevent OUD risks and support for clients on MOUD. Such interventions include mitigating adverse childhood experiences, supporting families, and creating safe community environments.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".