Qualitative health research plays a central role in exploring individuals’ experiences and perceptions of wellness, illness, and healthcare services. Visual tools are increasingly used for data elicitation. An ecomap is a visual tool that applies ecosystems theory to human communities and relationships to provide an illustration of the quality of relationships. We describe the use of ecomaps in qualitative health research. Searches across eight databases identified 407 citations. We screened them in duplicate to identify 129 publications that underwent full text review and included 73 in the final synthesis. We classified and summarized data based on iterative comparisons across sources. Benefits of using ecomaps include improving rapport and engagement with study participants, facilitating iterative question development, and highlighting the social contexts of relationships. When used in conjunction with interviews, they promote data credibility through triangulation. Investigators have used ecomaps as a tool to facilitate primary and secondary analysis of data. Researchers have adapted the ecomap to meet their health research needs. Challenges to their use include additional time and training needed to complete, and potential privacy and confidentiality concerns. Ecomaps can be useful in qualitative health research to enhance data elicitation, analysis, presentation, and to augment study rigor.
ObjectiveTo conduct a systematic review of observational studies on methamphetamine-associated heart failure (MethHF) .MethodsSix databases were searched for original publications on the topic. Title/abstract and included full-text publications were reviewed in duplicate. Data extraction and critical appraisal for risk of bias were performed in duplicate.ResultsTwenty-one studies are included in the final analysis. Results could not be combined because of heterogeneity in study design, population, comparator, and outcome assessment. Overall risk of bias is moderate due to the presence of confounders, selection bias and poor matching; overall certainty in the evidence is very low. MethHF is increasing in prevalence, affects diverse racial/ethnic/sociodemographic groups with a male predominance; up to 44% have preserved left-ventricular ejection fraction. MethHF is associated with significant morbidity including worse heart failure symptoms compared with non-methamphetamine related heart failure. Female sex, methamphetamine abstinence and guideline-directed heart failure therapy are associated with improved outcomes. Chamber dimensions on echocardiography and fibrosis on biopsy predict the extent of recovery after abstinence.ConclusionsThe increasing prevalence of MethHF with associated morbidity underscores the urgent need for well designed prospective studies of people who use methamphetamine to accurately assess the epidemiology, clinical features, disease trajectory and outcomes of MethHF. Methamphetamine abstinence is an integral part of MethHF treatment; increased availability of effective non-pharmacological interventions for treatment of methamphetamine addiction is an essential first step. Availability of effective pharmacological treatment for methamphetamine addiction will further support MethHF treatment. Using harm reduction principles in an integrated addiction/HF treatment programme will bolster efforts to stem the increasing tide of MethHF.
SummaryThe rapidly burgeoning quantity and complexity of publications makes curating and synthesizing information for meta-analyses ever more challenging. Meta-analyses require manual review of abstracts for study inclusion, which is time consuming, and variation among reviewer interpretation of inclusion/exclusion criteria for selecting a paper to be included in a review can impact a study’s outcome. To address these challenges in efficiency and accuracy, we propose and evaluate a machine learning approach to capture the definition of inclusion/exclusion criteria using a machine learning model to automate the selection process. We trained machine learning models on a manually reviewed dataset from a meta-analysis of resilience factors influencing psychopathology development. Then, the trained models were applied to an oncology dataset and evaluated for efficiency and accuracy against trained human reviewers. The results suggest that machine learning models can be used to automate the paper selection process and reduce the abstract review time while maintaining accuracy comparable to trained human reviewers. We propose a novel approach which uses model confidence to propose a subset of abstracts for manual review, thereby increasing the accuracy of the automated review while reducing the total number of abstracts requiring manual review. Furthermore, we delineate how leveraging these models more broadly may facilitate the sharing and synthesis of research expertise across disciplines.
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