Healthcare provider perspectives on the facilitators, barriers and unexplored opportunities to support the recovery of clients on medications for opioid use disorder in Kenya
Bibliographic record
Abstract
Abstract Introduction Opioid use is associated with significant burden of disease. Medications for opioid use disorder such as methadone are effective treatments. This study aimed to identify the healthcare provider perspectives on facilitators, barriers and unexplored opportunities toward achieving optimal clinical outcomes for clients on methadone treatment. Method The study conducted at a methadone treatment clinic in Kenya, used an exploratory study design to explore healthcare provider experiences of providing care to patients on methadone treatment. Interviews lasting an average of 30–40 min were transcribed verbatim and analyzed thematically using NVivo 12 software. Results Eleven participants were enrolled and three themes were identified namely: (a)The impact of methadone treatment on clients whereby methadone was perceived to possess transformative attributes enabling clients to regain some semblance of control of their lives including finding gainful employment and relationship restoration; (b) Threats to client recovery which include factors at the individual level such lack of training and factors at the system levels such as inadequate staff, and; (c) Thinking outside the box whereby participants proposed diverse strategies to support clients’ full engagement in care including private–public partnerships to support travel logistics. Conclusions Healthcare providers caring for clients on methadone treatment exhibited an understanding of the facilitators, barriers and opportunities for improving treatment outcomes for clients on methadone. Finding innovative solutions to mitigate the barriers identified can increase client retention and 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".