How do people who use drugs experience treatment? A qualitative analysis of views about opioid substitution treatment in primary care (iCARE study)
Bibliographic record
Abstract
OBJECTIVE: To understand the most significant aspects of care experienced by people in opioid substitution treatment (OST) in primary care settings. DESIGN: Semistructured individual interviews were conducted, following the critical incidents technique. Interview transcripts were analysed following a thematic analysis approach. PARTICIPANTS: Adults aged 18 years or older, receiving OST in UK-based primary care services. RESULTS: Twenty-four people in OST were interviewed between January and March 2019. Participants reported several aspects which were significant for their treatment, when engaging with the primary care service. These were grouped into 10 major themes: (1) humanised care; (2) individual bond/connection with the professional; (3) professionals' experience and knowledge; (4) having holistic care; (5) familiarity; (6) professionals' commitment and availability to help; (7) anonymity; (8) location; (9) collaborative teamwork; and (10) flexibility and changes around the treatment plan. CONCLUSIONS: This study included first-hand accounts of people who use drugs about what supports them in their recovery journey. The key lessons learnt from our findings indicate that people who use drugs value receiving treatment in humanised and destigmatised environments. We also learnt that a good relationship with primary care professionals supports their recovery journey, and that treatment plans should be flexible, tailor-made and collaboratively designed with patients.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".