State practitioner insights into local public health challenges and opportunities in obesity prevention: a qualitative study.

IntroductionThe extent of obesity prevention activities conducted by local health departments (LHDs) varies widely. The purpose of this qualitative study was to characterize how state obesity prevention program directors perceived the role of LHDs in obesity prevention and factors that impact LHDs' success in obesity prevention.MethodsFrom June 2011 through August 2011, we conducted 28 semistructured interviews with directors of federally funded obesity prevention programs at 22 state and regional health departments. Interviews were transcribed verbatim, coded, and analyzed to identify recurring themes and key quotations.ResultsMain themes focused on the roles of LHDs in local policy and environmental change and on the barriers and facilitators to LHD success. The role LHDs play in obesity prevention varied across states but generally reflected governance structure (decentralized vs centralized). Barriers to local prevention efforts included competing priorities, lack of local capacity, siloed public health structures, and a lack of local engagement in policy and environmental change. Structures and processes that facilitated prevention were having state support (eg, resources, technical assistance), dedicated staff, strong communication networks, and a robust community health assessment and planning process.ConclusionsThese findings provide insight into successful strategies state and local practitioners are using to implement innovative (and evidence-informed) community-based interventions. The change in the nature of obesity prevention requires a rethinking of the state-local relationship, especially in centralized states

Efficiency, Efficacy, and Power in the Implementation of a Medication Adherence Aid.

Nonadherence to medication regimens is common, with approximately 50% of patients not taking their medications as prescribed. The Universal Medication Schedule (UMS) is a set of standardized, evidence-based, and patient-centered instructions for pill-form medications that has demonstrated improvements in adherence by promoting patient comprehension. An urban, publicly funded, integrated health care system attempted to adopt UMS labeling but had limited success at its largest pilot site, which was a safety-net health care system's outpatient pharmacy. To assess barriers to implementation, we engaged pharmacists at this site in group interviews. We thematically analyzed transcripts by integrating sociological work on standardization with grounded theory methodologies. In addition to lacking technological infrastructure, tensions among efficiency, efficacy, and effectiveness, and tension between individual/biomedical versus population health perspectives emerged as barriers to implementation. Additionally, we discovered that hierarchies of professional power impeded uptake. For successful implementation of evidence-based practices for vulnerable populations in resource-poor settings, efforts must anticipate and reconcile the tensions among conflicting demands, professional hierarchies, and divergent orientations to patient care. [HLRP: Health Literacy Research and Practice. 2018;2(3):e128-e131.]

Models in dissemination and implementation research: useful tools in public health services and systems research

Dissemination and implementation (D&I) science seeks to understand how to systematically facilitate utilization of evidence. Theories and frameworks (hereafter called models) augment D&I research by enhancing efforts to spread evidence-based interventions (EBIs). D&I models are relevant for public health services and systems research, which also explores the uses of evidence. This report: 1) develops an inventory of models; 2) synthesizes this information; and 3) provides guidance on how to select a model. The research team used snowball sampling to collect models. This article uses three author-defined variables: construct flexibility, focus on dissemination and/or implementation activities, and socio-ecological framework level to categorize models. Models that addressed policy are noted. Public health researchers and practitioners can use this inventory to identify models to guide D&I research and/or efforts

Physician Detection of Clinical Harm in Machine Translation: Quality Estimation Aids in Reliance and Backtranslation Identifies Critical Errors

A major challenge in the practical use of Machine Translation (MT) is that users lack guidance to make informed decisions about when to rely on outputs. Progress in quality estimation research provides techniques to automatically assess MT quality, but these techniques have primarily been evaluated in vitro by comparison against human judgments outside of a specific context of use. This paper evaluates quality estimation feedback in vivo with a human study simulating decision-making in high-stakes medical settings. Using Emergency Department discharge instructions, we study how interventions based on quality estimation versus backtranslation assist physicians in deciding whether to show MT outputs to a patient. We find that quality estimation improves appropriate reliance on MT, but backtranslation helps physicians detect more clinically harmful errors that QE alone often misses.Comment: EMNLP 202