How injectable opioid agonist treatment (iOAT) care could be improved? service providers and stakeholders’ perspectives
Bibliographic record
Abstract
Background Addressing inadequacies in the implementation of existing evidence-based approaches into practice, such as injectable opioid agonist treatment (iOAT), is imperative for the management of opioid use disorder. With the expansion of iOAT, stakeholder perspectives are needed to inform program optimization. This study aimed to understand stakeholder and provider perspectives on iOAT care and how it can be improved to better meet service users’ needs.Methods Semi-structured interviews (n = 11), email correspondence (n = 2), a focus group (n = 4), and one regional meeting were conducted with iOAT stakeholders to receive feedback on how iOAT can better meet service users’ needs. Qualitative analysis employed a thematic and interpretive description approach to identify key themes, presented as a thematic summary.Results Stakeholder narratives highlight the importance they attribute to client autonomy, individualized care, tensions between providers and the system (policies, governing structures, etc., that establish, facilitate and determine how iOAT is delivered in Canada) as well as power dynamics between providers and service users.Conclusion IOAT providers and stakeholders surveyed in this study are committed to seeing the needs of service users met but often feel constrained by system-level regulations that influence power dynamics between providers and service users. Findings underline pragmatic suggestions to advance person-centered care as a way to make iOAT accessible and individualized.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".