“They Talk to Me Like a Person” Experiences of People in an Injectable Opioid Agonist Treatment Program
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to explore client experiences in a community-based injectable opioid agonist therapy (iOAT) program. STUDY SETTING: The study occurred across two cities in Alberta, Canada. STUDY DESIGN: The research team conducted secondary interpretive description analysis on qualitative interview transcripts. DATA COLLECTION: Twenty-three iOAT clients were interviewed as part of a prior quality improvement initiative. Using secondary analysis of the transcripts, interviews were analyzed for themes, to create an understanding of clients' experiences. PRINCIPAL FINDINGS: Participants accessed iOAT through other health services, for treatment of opioid use disorder. Participants reported that building trusting and supportive relationships with nurses was crucial to their success in the program. Through these relationships, participants experienced stopping and starting. They stopped behaviors such as illicit drug use, having withdrawal symptoms and anxiety, and prohibited income generation activities. They started taking care of themselves, accessing housing, increasing financial stability, receiving primary care, and connecting with friends and family. The global experience of iOAT was one of positive change for participants. CONCLUSIONS: The findings of this study are largely consistent with other published examples-iOAT programs create benefits for both clients and their communities. Although clients may join the program to access the hydromorphone, the relationships between staff and clients are the key driver of success.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".