Experiences of Healthcare Professionals Working in Injectable Opioid Agonist Treatment Programs
Bibliographic record
Abstract
BACKGROUND: Injectable opioid agonist treatment (iOAT) programs are increasing as a method of harm reduction for opioid use disorder. Although there have been numerous studies of client experience in iOAT programs, there have been few studies on the experiences on healthcare professionals working in these programs. AIM: In this study, we aimed to understand the experiences and perspectives of healthcare professionals in iOAT programs. This study is among the first to explore the experiences of healthcare professionals in an operational iOAT program, with the aim of making workforce recommendations to enhance the sustainability of iOAT programs. METHODS: We conducted a secondary analysis using a thematic analysis approach with qualitative interview transcripts. RESULTS: Sixteen participants were interviewed, and we analyzed the transcripts, identifying three major themes: healthcare professionals' experiences in the iOAT program, approaches to work, and navigating practice issues. Working in iOAT was rewarding for participants because of the changes the program created in clients' lives. Participants reported that building trusting relationships with iOAT clients was key to the client's success. Healthcare professionals' approaches to their work varied, where they adopted either client-centered care or rules-based approaches. Healthcare professionals' experiences were shaped by program structure, the need to adapt their work, and building relationships with other healthcare services. Managing limited resources was a challenge for participants. CONCLUSION: Supportive work environments can foster relationships between healthcare professionals and clients, for success in iOAT programs. Healthcare professionals require adequate support and staffing to provide high-quality care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".