Exploring health-care providers’ experiences in the care of clients on opioid agonist treatment in two western Canadian clinics
Bibliographic record
Abstract
Introduction Prescribing methadone as part of opioid agonist therapy is a common treatment approach to manage opioid use disorder. Unfortunately, many clients prematurely discontinue opioid agonist therapy because of restrictions attached to the therapy. Purpose The purpose of this study was to explore health-care provider experiences as they worked with clients on opioid agonist therapy in a western Canadian city. Methods In this descriptive, qualitative study, we interviewed 18 health-care providers working in an opioid agonist therapy setting. The focus of the interviews was on the organization of opioid agonist therapy care at their clinic, their personal experiences and challenges faced when providing care to their clients. Interviews were recorded electronically and transcribed verbatim and thematic analysis was completed using NVIVO software. Results The following three themes emerged from the data relate to the care organization and health care provider (HCP)-clients dynamics. These are: (1) fragmentation of care for a complex problem, (2) enforcing compliance to treatment, and (3) the importance of a therapeutic alliance to improve overall care. Conclusion The opioid agonist therapy model is biocentric and emphasizes abstinence which can create tension between providers and clients. Making the model of care more patient centred might help to improve client retention rates and successful treatment outcomes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".