Self‐managing illicit stimulant use: A qualitative study with patients receiving injectable opioid agonist treatment
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Illicit stimulant use is prevalent among patients receiving injectable opioid agonist treatment (iOAT) and has been associated with early treatment discontinuation and illicit opioid use. Despite these concerns, little is known about the use of illicit stimulants in this population. As such, this study aimed to explore the processes by which patients receiving iOAT engage in the use of illicit stimulants. DESIGN AND METHODS: One-on-one in-depth qualitative interviews were conducted. Data collection and analysis followed an iterative approach of coding, searching for meaning, and returning to data collection to saturate categories and explicate relationships between them. Participants were patients receiving iOAT in Vancouver, Canada that reported the use of illicit stimulants (n = 31). RESULTS: The process of 'self-managing illicit stimulant use' was constructed from the data. This process was made up of three interrelated categories reflecting participants' engagement in illicit stimulant use: (i) distancing from the street environment; (ii) taking control of use; and (iii) mobilising support (clinical and social). DISCUSSION AND CONCLUSIONS: For patients with opioid use disorder and concurrent stimulant use disorder, access to iOAT can promote the self-management of illicit stimulant use. Daily visits to the clinic for opioid agonist treatment present an important opportunity to offer services and supports for patients who use illicit stimulants. Interventions can be guided by patients, recognising them as experts in the management of their stimulant 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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".