Tailoring dissemination strategies to increase evidence-informed policymaking for opioid use disorder treatment: study protocol
Bibliographic record
Abstract
BACKGROUND: Policy is a powerful tool for systematically altering healthcare access and quality, but the research to policy gap impedes translating evidence-based practices into public policy and limits widespread improvements in service and population health outcomes. The US opioid epidemic disproportionately impacts Medicaid members who rely on publicly funded benefits to access evidence-based treatment including medications for opioid use disorder (MOUD). A myriad of misaligned policies and evidence-use behaviors by policymakers across federal agencies, state Medicaid agencies, and managed care organizations limit coverage of and access to MOUD for Medicaid members. Dissemination strategies that improve policymakers' use of current evidence are critical to improving MOUD benefits and reducing health disparities. However, no research describes key determinants of Medicaid policymakers' evidence use behaviors or preferences, and few studies have examined data-driven approaches to developing dissemination strategies to enhance evidence-informed policymaking. This study aims to identify determinants and intermediaries that influence policymakers' evidence use behaviors, then develop and test data-driven tailored dissemination strategies that promote MOUD coverage in benefit arrays. METHODS: Guided by the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework, we will conduct a national survey of state Medicaid agency and managed care organization policymakers to identify determinants and intermediaries that influence how they seek, receive, and use research in their decision-making processes. We will use latent class methods to empirically identify subgroups of agencies with distinct evidence use behaviors. A 10-step dissemination strategy development and specification process will be used to tailor strategies to significant predictors identified for each latent class. Tailored dissemination strategies will be deployed to each class of policymakers and assessed for their acceptability, appropriateness, and feasibility for delivering evidence about MOUD benefit design. DISCUSSION: This study will illuminate key determinants and intermediaries that influence policymakers' evidence use behaviors when designing benefits for MOUD. This study will produce a critically needed set of data-driven, tailored policy dissemination strategies. Study results will inform a subsequent multi-site trial measuring the effectiveness of tailored dissemination strategies on MOUD benefit design and implementation. Lessons from dissemination strategy development will inform future research about policymakers' evidence use preferences and offer a replicable process for tailoring dissemination strategies.
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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 | 0.073 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.094 | 0.018 |
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".