Mal/adaptations: A qualitative evidence synthesis of opioid agonist therapy during major disruptions
Bibliographic record
Abstract
BACKGROUND: Opioid agonist therapy (OAT) has been severely disrupted by the COVID-19 pandemic. The risks of opioid withdrawal, overdose, and diversion have increased, so there is an urgent need to adapt OAT to best support people who use drugs (PWUD). This review examines the views and experiences of PWUD, health care providers, and health system administrators on OAT during major disruptions to medical care to inform appropriate health system responses during the current pandemic and beyond. METHODS: We conducted a systematic review and qualitative evidence synthesis. We searched three comprehensive datasets for qualitative and mixed-methods studies that examined OAT in the context of major disruptions such as natural disasters, and analyzed included studies using thematic analysis and the constant comparative method. We used conceptual frameworks of health systems resilience and adaptive systems to interpret our findings. RESULTS: We included 10 studies published between 2002 and 2020 that examined OAT in the context of hurricanes, earthquakes, and terrorist attacks. We organized our results into three themes: uncertainty, inconsistency, and vulnerability; regulatory inflexibility; and lack of coordination. The highly regulated but poorly coordinated systems of OAT provision lacked flexibility to adapt to major disruptions, thereby manufacturing vulnerability for both PWUD and health workers. CONCLUSIONS: OAT programs must be resilient and adaptable to face major disruptions while maintaining quality care. Our findings provide guidance to develop and implement innovative strategies that increase the adaptive potential of OAT programs while focusing on the needs of PWUD.
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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 | 0.075 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| 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".