Bridging the Macro-micro Divide: A Qualitative Meta-synthesis on the Perspectives and Experiences of Health Care Providers on the Extramedical Use and Diversion of Buprenorphine
Bibliographic record
Abstract
OBJECTIVES: Opioid agonist therapy using buprenorphine is one of the most effective treatments for opioid use disorder. However, concerns regarding its extramedical use and diversion, such as adverse patient outcomes and damage to the legitimacy of addictions practice, are persistent. The aim of this review is to synthesize the perspectives and experiences of health care providers around the extramedical use of buprenorphine. METHODS: A qualitative meta-synthesis was conducted based on a systematic search of 8 databases. All primary qualitative and mixed-methods studies relating to the views of health care providers on the extramedical use of buprenorphine were included. A qualitative analysis informed by the constant comparative method was conducted, using NVivo for data management. RESULTS: Sixteen studies were included in this review. Findings were organizedunder 2 key themes: (1) Harm-producing versus harm-reducing effects of extramedical buprenorphine use and (2) driving forces of and responses to extramedical buprenorphine use. CONCLUSIONS: The studies included in our review identified a disconnect-health care providers noted that macro, health care system-level challenges drove extramedical use whereas the recommended solutions for prevention and management were primarily aimed at the micro, individual level. This study emphasizes the critical role that health care providers can play, in partnership with patients, in informing appropriate policies and health care system design to optimize the care for people with opioid use disorder.
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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 | 0.083 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| 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".