ABSTRACT
Community health worker (CHW) programs are an increasingly popular strategy for patient-centered care. Many health care organizations are building CHW programs through trial and error, rather than implementing or adapting evidence-based interventions. This study used a qualitative design-mapping process to adapt an evidence-based CHW intervention, originally developed and tested in the hospital setting, for use among outpatients with multiple chronic conditions. The study involved qualitative in-depth, semi-structured interviews with chronically ill, uninsured, or Medicaid outpatients from low-income zip codes (n = 21) and their primary care practice staff (n = 30). Three key themes informed adaptation of the original intervention for outpatients with multiple conditions. First, outpatients were overwhelmed by their multiple conditions and wished they could focus on 1 at a time. Thus, the first major revision was to design a low-literacy decision aid that patients and providers could use to select a condition to focus on during the intervention. Second, motivation for health behavior change was a more prominent theme than in the original intervention. It was decided that in addition to providing tailored social support as in the original intervention, CHWs would help patients track progress toward their chronic disease management goals to motivate health behavior change. Third, patients were already connected to primary care; yet they still needed additional support to navigate their clinic once the intervention ended. The intervention was revised to include a weekly clinic-based support group. Structured adaptation using qualitative design mapping may allow for rapid adaptation and scale-up of evidence-based CHW interventions across new settings and populations.
Subject(s)
Community Health Workers , Models, Theoretical , Outpatients/psychology , Patient-Centered Care , Health Behavior , Humans , Interviews as Topic , Multiple Chronic Conditions , Primary Health Care , Qualitative ResearchSubject(s)
Law Enforcement/history , Police/psychology , Racism/history , Racism/prevention & control , Students, Medical/psychology , Violence/prevention & control , Violence/psychology , Adolescent , Adult , Black or African American/psychology , Aged , Aged, 80 and over , Female , History, 20th Century , History, 21st Century , Humans , Male , Middle Aged , Social Justice/history , Substance-Related Disorders/history , United States , Young AdultABSTRACT
IMPORTANCE: Grammatical comprehension difficulty is an essential supporting feature of the non-fluent/agrammatic variant of primary progressive aphasia (naPPA), but well-controlled clinical measures of grammatical comprehension are unavailable. OBJECTIVE: To develop a measure of grammatical comprehension and examine this comparatively in PPA variants and behavioural-variant frontotemporal degeneration (bvFTD) and to assess the neuroanatomic basis for these deficits with volumetric grey matter atrophy and whole-brain fractional anisotropy (FA) in white matter tracts. DESIGN: Case-control study. SETTING: Academic medical centre. PARTICIPANTS: 39 patients with variants of PPA (naPPA=12, lvPPA=15 and svPPA=12), 27 bvFTD patients without aphasia and 12 healthy controls. MAIN OUTCOME MEASURE: Grammatical comprehension accuracy. RESULTS: Patients with naPPA had selective difficulty understanding cleft sentence structures, while all PPA variants and patients with bvFTD were impaired with sentences containing a centre-embedded subordinate clause. Patients with bvFTD were also impaired understanding sentences involving short-term memory. Linear regressions related grammatical comprehension difficulty in naPPA to left anterior-superior temporal atrophy and reduced FA in corpus callosum and inferior frontal-occipital fasciculus. Difficulty with centre-embedded sentences in other PPA variants was related to other brain regions. CONCLUSIONS AND RELEVANCE: These findings emphasise a distinct grammatical comprehension deficit in naPPA and associate this with interruption of a frontal-temporal neural network.