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Abstract Background In 2014, the National Institute of Health Pain Consortium’s research task force (RTF) on research standards for chronic low back pain (CLBP) proposed the Impact Stratification Score (ISS) as a patient-reported outcome measure that could stratify patients by the impact CLBP has on their lives. This work compares three newly developed ISS-based classifications to the RTF’s original to provide an optimal recommendation. Methods The online sample included 1226 individuals from Amazon’s Mechanical Turk who indicated having non-specific CLBP, average age of 40, 49% female, and 67% White. Participants completed the PROMIS-29 v2.1 profile survey that contains the 9 ISS items as well the Roland-Morris Disability Questionnaire (RMDQ) and Graded Chronic Pain Scale (GCPS). Other items included high-impact chronic pain; not working due to health problems; overall health; and number of healthcare visits for back pain in the past 6 months. Three new classifications were created using quartiles (Classification 2), latent profile analysis (Classification 3), and one modeled after the GCPS (Classification 4). Classifications were subsequently compared to the RTF-proposed classification (Classification 1) on several concurrent and prognostic criteria. Results Classification 1 had three CLBP severity groups, four in Classification 2, three in Classification 3, and four in Classification 4. All novel classifications improved upon the original. Classification 2 performed best at minimizing the classification of those with negative outcomes into the lowest severity groups at baseline (e.g., 11% with RMDQ ≥ 7) and 6 months (e.g., 8.2% had fair/poor health). Classification 4 performed best at maximizing classification of those with negative outcomes into the most severe group concurrently (e.g., 100% had GCPS grade ≥ 2) and at 6 months (e.g., 100% with RMDQ ≥ 7). Conclusions We developed three ISS-based classification schemes and tested them against several outcomes. All three improved upon the original scheme. While appearing more optimal than other classifications in the lowest severity groups, Classification 2 presents some considerations and limitations. Given that Classification 4 was an improvement at the lowest end of severity and was the best at the highest end, it is our tentative recommendation that this approach be adopted to classify individuals with non-specific CLBP.

Abstract Background Implementation support models are increasingly being used to enhance the delivery of evidence-based treatments (EBTs) in routine care settings. Little is known about the extent to which these models lead to continued EBT use after implementation support ends. Moreover, few empirical studies longitudinally examine the hypothesized factors associated with long-term psychosocial EBT use (i.e., sustainment). In an effort to address this gap, the current study examined sustainment of an EBT called the Adolescent-Community Reinforcement Approach (A-CRA) following the end of implementation support. Methods Between 2006 and 2010, the Substance Abuse and Mental Health Services Administration awarded 3 years of A-CRA implementation support to 82 community-based organizations around the USA. The extent to which A-CRA was sustained following grant end and the hypothesized factors associated with EBT sustainment were collected using both retrospective and prospective data. We examined the extent to which 10 core treatment elements of A-CRA were sustained and the associations between the extent of A-CRA sustainment and hypothesized factors using a pattern-mixture longitudinal modeling approach. Results Staff from 76 organizations participated in data collection for a 92.86% response rate. On average, about half of the 10 core treatment elements were sustained following the loss of implementation support. Factors that appeared most important to A-CRA sustainment included characteristics that were related to the outer setting (communication, funding, and partnerships), inner setting (political support, organizational capacity, and supervisor turnover rate), implementation support period (number of clinicians and supervisors certified and employed at support end and number of youth served), and staff perceptions of the intervention (implementation difficulty, relative advantage, and perceived success). Conclusions Even with multiple years of implementation support, community-based organizations face challenges in sustaining EBT delivery over time. Consistent with implementation theories, multiple factors appear related with EBT sustainment, including the degree of implementation during the initial support period, as well as adequate funding, infrastructure support, and staff support following the end of funding.

BackgroundResearchers have many options for web-based survey data collection, ranging from access to curated probability-based panels, where individuals are selectively invited to join based on their membership in a representative population, to convenience panels, which are open for anyone to join. The mix of respondents available also varies greatly regarding representation of a population of interest and in motivation to provide thoughtful and accurate responses. Despite the additional dataset-building labor required of the researcher, convenience panels are much less expensive than probability-based panels. However, it is important to understand what may be given up regarding data quality for those cost savings. ObjectiveThis study examined the relative costs and data quality of fielding equivalent surveys on Amazon’s Mechanical Turk (MTurk), a convenience panel, and KnowledgePanel, a nationally representative probability-based panel. MethodsWe administered the same survey measures to MTurk (in 2021) and KnowledgePanel (in 2022) members. We applied several recommended quality assurance steps to enhance the data quality achieved using MTurk. Ipsos, the owner of KnowledgePanel, followed their usual (industry standard) protocols. The survey was designed to support psychometric analyses and included >60 items from the Patient-Reported Outcomes Measurement Information System (PROMIS), demographics, and a list of health conditions. We used 2 fake conditions (“syndomitis” and “chekalism”) to identify those more likely to be honest respondents. We examined the quality of each platform’s data using several recommended metrics (eg, consistency, reliability, representativeness, missing data, and correlations) including and excluding those respondents who had endorsed a fake condition and examined the impact of weighting on representativeness. ResultsWe found that prescreening in the MTurk sample (removing those who endorsed a fake health condition) improved data quality but KnowledgePanel data quality generally remained superior. While MTurk’s unweighted point estimates for demographics exhibited the usual mismatch with national averages (younger, better educated, and lower income), weighted MTurk data matched national estimates. KnowledgePanel’s point estimates better matched national benchmarks even before poststratification weighting. Correlations between PROMIS measures and age and income were similar in MTurk and KnowledgePanel; the mean absolute value of the difference between each platform’s 137 correlations was 0.06, and 92% were