Scientists and practitioners alike need reliable, valid measures of contextual factors that influence implementation. Yet, few existing measures demonstrate reliability or validity. To meet this need, we developed and assessed the psychometric properties of measures of several constructs within the Inner Setting domain of the Consolidated Framework for Implementation Research (CFIR). We searched the literature for existing measures for the 7 Inner Setting domain constructs (Culture Overall, Culture Stress, Culture Effort, Implementation Climate, Learning Climate, Leadership Engagement, and Available Resources). We adapted items for the healthcare context, pilot-tested the adapted measures in 4 Federally Qualified Health Centers (FQHCs), and implemented the revised measures in 78 FQHCs in the 7 states (N = 327 respondents) with a focus on colorectal cancer (CRC) screening practices. To psychometrically assess our measures, we conducted confirmatory factor analysis models (CFA; structural validity), assessed inter-item consistency (reliability), computed scale correlations (discriminant validity), and calculated inter-rater reliability and agreement (organization-level construct reliability and validity). CFAs for most constructs exhibited good model fit (CFI > 0.90, TLI > 0.90, SRMR < 0.08, RMSEA < 0.08), with almost all factor loadings exceeding 0.40. Scale reliabilities ranged from good (0.7 ≤ α < 0.9) to excellent (α ≥ 0.9). Scale correlations fell below 0.90, indicating discriminant validity. Inter-rater reliability and agreement were sufficiently high to justify measuring constructs at the clinic-level. Our findings provide psychometric evidence in support of the CFIR Inner Setting measures. Our findings also suggest the Inner Setting measures from individuals can be aggregated to represent the clinic-level. Measurement of the Inner Setting constructs can be useful in better understanding and predicting implementation in FQHCs and can be used to identify targets of strategies to accelerate and enhance implementation efforts in FQHCs.
Background: A number of studies have examined the influence of self-efficacy, social support and patient-provider communication (PPC) on self-care and glycemic control. Relatively few studies have tested the pathways through which these constructs operate to improve glycemic control, however. We used structural equation modeling to examine a conceptual model that hypothesizes how self-efficacy, social support and patient-provider communication influence glycemic control through self-care behaviors in Chinese adults with type 2 diabetes. Methods: We conducted a cross-sectional study of 222 Chinese adults with type 2 diabetes in one primary care center. We collected information on demographics, self-efficacy, social support, patient-provider communication (PPC) and diabetes self-care. Hemoglobin A1c (HbA1c) values were also obtained. Measured variable path analyses were used to determine the predicted pathways linking self-efficacy, social support and PPC to diabetes self-care and glycemic control. Results: Diabetes self-care had a direct effect on glycemic control (β = −0.21, p = .007), No direct effect was observed for self-efficacy, social support or PPC on glycemic control. There were significant positive direct paths from self-efficacy (β = 0.32, p < .001), social support (β = 0.17, p = .009) and PPC (β = 0.14, p = .029) to diabetes self-care. All of them had an indirect effect on HbA1c (β =–0.06, β =–0.04, β =–0.03 respectively). Additionally, PPC was positively associated with social support (γ = 0.32, p < .001). Conclusions: Having better provider-patient communication, having social support, and having higher self-efficacy was associated with performing diabetes self-care behaviors; and these behaviors were directly linked to glycemic control. So longitudinal studies are needed to explore the effect of self-efficacy, social support and PPC on changes in diabetes self-care behaviors and glycemic control.
Objectives. We tested the efficacy of a minimal intervention to create smoke-free homes in low-income households recruited through the United Way of Greater Atlanta 2-1-1, an information and referral system that connects callers to local social services. Methods. We conducted a randomized controlled trial (n = 498) from June 2012 through June 2013, with follow-up at 3 and 6 months. The intervention consisted of 3 mailings and 1 coaching call. Results. Participants were mostly smokers (79.7%), women (82.7%), African American (83.3%), and not employed (76.5%), with an annual household income of $10 000 or less (55.6%). At 6-months postbaseline, significantly more intervention participants reported a full ban on smoking in the home than did control participants (40.0% vs 25.4%; P = .002). The intervention worked for smokers and nonsmokers, as well as those with or without children. Conclusions. Minimal intervention was effective in promoting smoke-free homes in low income households and offers a potentially scalable model for protecting children and adult nonsmokers from secondhand smoke exposure in their homes.
Implementing evidence-based practices (EBPs) to increase cancer screenings in safety net primary care systems has great potential for reducing cancer disparities. Yet there is a gap in understanding the factors and mechanisms that influence EBP implementation within these high-priority systems. Guided by the Consolidated Framework for Implementation Research (CFIR), our study aims to fill this gap with a multiple case study of health care safety net systems that were funded by an American Cancer Society (ACS) grants program to increase breast and colorectal cancer screening rates. The initiative funded 68 safety net systems to increase cancer screening through implementation of evidence-based provider and client-oriented strategies. Data are from a mixed-methods evaluation with nine purposively selected safety net systems. Fifty-two interviews were conducted with project leaders, implementers, and ACS staff. Funded safety net systems were categorized into high-, medium-, and low-performing cases based on the level of EBP implementation. Within- and cross-case analyses were performed to identify CFIR constructs that influenced level of EBP implementation. Of 39 CFIR constructs examined, six distinguished levels of implementation. Two constructs were from the intervention characteristics domain: adaptability and trialability. Three were from the inner setting domain: leadership engagement, tension for change, and access to information and knowledge. Engaging formally appointed internal implementation leaders, from the process domain, also distinguished level of implementation. No constructs from the outer setting or individual characteristics domain differentiated systems by level of implementation. Our study identified a number of influential CFIR constructs and illustrated how they impacted EBP implementation across a variety of safety net systems. Findings may inform future dissemination efforts of EBPs for increasing cancer screening in similar settings. Moreover, our analytic approach is similar to previous case studies using CFIR and hence could facilitate comparisons across studies.