Global health and rehabilitation education in Canada: a qualitative analysis of experiences and perspectives of educators.

BACKGROUND: Rehabilitation practice is increasingly tied to global trends. First, healthcare services are increasingly tailored to be patient-centered, requiring knowledge of sociocultural contexts and experiences of a diverse patient population. Second, non-communicable diseases and morbidity stemming from infectious diseases are creating greater needs for rehabilitation services in countries facing this double burden of disease. Third, globalization continues to shape the risk factors for disease and disability and influences the type of services accessible and the financing and management of such services. Given this context, there is a critical need to examine how global health (GH) is approached in rehabilitation curricula. How students learn about these various dynamics will impact their ability to practice in this environment and best meet the needs of the patients and populations they are caring for. OBJECTIVES: This study explores how university-level Canadian occupational and physical therapy educators understand GH within their academic and clinical practices. METHODS: The project followed a qualitative design. Semi-structured interviews were conducted with participants (n = 12; randomized purposive sampling was used). A deductive and inductive thematic analysis was performed to describe the participants perspectives on and practices of GH education in rehabilitation education programs. FINDINGS: There was an overall coherence between the literature and the participant's views of the constituent elements of GH. Participants viewed GH as a framework to broaden the clinical training of students by encouraging a critical "clinical toolbox" via themes of structural competency, cultural sensitivity, and a sense of global citizenship and stewardship. However, GH was also described by participants as being implicitly present in the curriculum due to ambiguities regarding the scope of GH, seen as a key obstacle in the integration of GH education in rehabilitation programs. INTERPRETATION: Despite the growing interest and relevance of GH, there is a noteworthy absence of GH education guidelines. There is a need to uncover the scope and underpinnings of GH and to outline rehabilitation-oriented GH competencies as per the positive value of GH attributed by the educators to the training of our healthcare workforce.IMPLICATIONS FOR REHABILITATIONGlobal health is seen by a sample of educators to provide rehabilitation students with a critical and holistic "clinical toolkit" by introducing themes of structural competency, cultural sensitivity and awareness, and global stewardship, enabling them to become good global clinicians and global citizens;The increased interest in global health throughout academic curricula presents new challenges for educators and students, specifically relating to understanding the mission and aims of this field. There is a need to clarify the notion and field of global health and how it complements clinical training within rehabilitation programs;There is a need to advance the literature on global health in rehabilitation by exploring what competencies are seen to be relevant to the field and practice of rehabilitation.

A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder.

INTRODUCTION: The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools. METHODS: The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria. RESULTS: An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility. LIMITATIONS: Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor. CONCLUSION: Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.