“It feels like I’m coming to a friend’s house”: an interpretive descriptive study of an integrated care site offering iOAT (Dr. Peter Centre)
Bibliographic record
Abstract
BACKGROUND: Injectable opioid agonist treatment (iOAT) has proven to be a safe and effective treatment option for severe opioid use disorder (OUD). Yet, iOAT is often isolated from other health and social services. To align with a person-centered care approach, iOAT can be embedded in sites that combine systems and services that have been historically fragmented and that address multiple comorbidities (integrated care sites). The present study investigates the addition of iOAT at an integrated care in Vancouver, British Columbia. We aimed to capture what it means for service users and service providers to incorporate iOAT in an integrated care site and describe the processes by which the site keeps people engaged. METHODS: We conducted 22 interviews with 15 service users and 14 interviews with 13 service providers across two rounds of individual semi-structured interviews (Fall 2021, Summer 2022). The second interview round was precipitated by a service interruption in medication dispensation. Interview audio was recorded, transcribed, and then analysed in NVivo 1.6 following an interpretive description approach. RESULTS: The emergent themes from the analysis are represented in two categories: (1) a holistic approach (client autonomy, de-medicalized care, supportive staff relationships, multiple opportunities for engagement, barriers to iOAT integration) and (2) a sense of place (physical location, social connection and community belonging, food). CONCLUSION: Incorporating iOAT at an integrated care site revealed how iOAT delivery can be strengthened through its direct connection to a diverse, comprehensive network of health and social services that are provided in a community atmosphere with high quality therapeutic relationships.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".