Education as drug policy: A realist synthesis of continuing professional development for opioid agonist therapy
Bibliographic record
Abstract
BACKGROUND: Continuing professional development (CPD) for opioid agonist therapy (OAT) has been identified as a key health policy strategy to improve care for people living with opioid use disorder (OUD) and to address rising opioid-related harms. To design and deliver effective CPD programs, there is a need to clarify how they work within complex health system and policy contexts. This review synthesizes the literature on OAT CPD programs and educational theory to clarify which interventions work, for whom, and in what contexts. METHODS: A systematic review and realist synthesis of evaluations of CPD programs focused on OAT was conducted. This included record identification and screening, theory familiarization, data collection, analysis, expert consultation, and iterative context-intervention-mechanism-outcome (CIMO) configuration development. RESULTS: Twenty-four reports comprising 21 evaluation studies from 5 countries for 3373 providers were reviewed. Through iterative testing of included studies with relevant theory, five CIMO configurations were developed. The programs were categorized by who drove the learning outcomes (i.e., system/policy, instructor, learner) and their spheres of influence (i.e., micro, meso, macro). There was a predominance of instructor-driven programs driving change at the micro level, with few policy-driven macro-influential programs, inconsistent with the promotion of CPD as a clear opioid crisis policy-level intervention. CONCLUSION: OAT CPD is challenged by mismatches in program justifications, objectives, activities, and outcomes. Depending on how these program factors interact, OAT CPD can operate as a barrier or facilitator to OUD care. With more deliberate planning and consideration of program theory, programs more directly addressing diverse learner and system needs may be developed and delivered. OAT CPD as drug policy does not operate in isolation; programs may feed into each other and intercalate with other policy initiatives to have micro, meso, and macro impacts on educational and population health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.076 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".