ArticleImplementation science communications2023
Tailoring dissemination strategies to increase evidence-informed policymaking for opioid use disorder treatment: study protocol.
Article in Implementation science communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- A systematic review of experimentally tested implementation strategies across health and human service settings: evidence from 2010-2022.Implementation science : IS · 2024Pooled it
- Systemic barriers and stigma: Healthcare provider perspectives on perinatal and neonatal care in the fentanyl crisis.Journal of substance use and addiction treatment · 2025Article
- A data-driven approach to implementing the HPTN 094 complex intervention INTEGRA in local communities.Implementation science : IS · 2024Article
- The "D&I Bridge": introducing a teaching tool to define the D, the I, and the why.Implementation science communications · 2024Article
- Design considerations for developing measures of policy implementation in quantitative evaluations of public health policy.Frontiers in health services · 2024Article
- Advancing healthcare equity through dissemination and implementation science.Health services research · 2023Review
- Using audience segmentation to identify implementation strategies to improve PrEP uptake among at-risk cisgender women: a mixed-methods study protocol.Implementation science communications · 2023Article
- Who's "in the room where it happens"? A taxonomy and five-step methodology for identifying and characterizing policy actors.Implementation science communications · 2023Article
- Four very basic ways to think about policy in implementation science.Implementation science communications · 2023Article
- A policy implementation study of earmarked taxes for mental health services: study protocol.Implementation science communications · 2023Article
- Correction: Tailoring dissemination strategies to increase evidence-informed policymaking for opioid use disorder treatment: study protocol.Implementation science communications · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundPolicy 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.
methodsGuided 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.
Indexed as
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.