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      Teaching the Social Determinants of Health in Undergraduate Medical Education: a Scoping Review

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          Abstract

          To provide optimal care, medical students should understand that the social determinants of health (SDH) impact their patients’ well-being. Those charged with teaching SDH to future physicians, however, face a paucity of curricular guidance. This review’s objective is to map key characteristics from publications about teaching SDH to students in undergraduate medical education (UME). In 2016, the authors searched PubMed, Embase, Web of Science, the Cochrane and ERIC databases, bibliographies, and MedEdPORTAL for articles published between January 2010 and November 2016. Four reviewers screened articles for eligibility then extracted and analyzed data descriptively. Scoping review methodology was used to map key concepts and curricular logistics as well as educator and student characteristics. The authors screened 3571 unique articles of which 22 were included in the final review. Many articles focused on community engagement (15). Experiential learning was a common instructional strategy (17) and typically took the form of community or clinic-based learning. Nearly half (10) of the manuscripts described school-wide curricula, of which only three spanned a full year. The majority of assessment was self-reported (20) and often related to affective change. Few studies objectively assessed learner outcomes (2). The abundance of initial articles screened highlights the growing interest in SDH in medical education. The small number of selected articles with sufficient detail for abstraction demonstrates limited SDH curricular dissemination. A lack of accepted tools or practices that limit development of robust learner or program evaluation was noted. Future research should focus on identifying and evaluating effective instructional and assessment methodologies to address this gap, exploring additional innovative teaching frameworks, and examining the specific contexts and characteristics of marginalized and underserved populations and their coverage in medical education. The online version of this article (10.1007/s11606-019-04876-0) contains supplementary material, which is available to authorized users.

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          The social determinants of health: coming of age.

          In the United States, awareness is increasing that medical care alone cannot adequately improve health overall or reduce health disparities without also addressing where and how people live. A critical mass of relevant knowledge has accumulated, documenting associations, exploring pathways and biological mechanisms, and providing a previously unavailable scientific foundation for appreciating the role of social factors in health. We review current knowledge about health effects of social (including economic) factors, knowledge gaps, and research priorities, focusing on upstream social determinants-including economic resources, education, and racial discrimination-that fundamentally shape the downstream determinants, such as behaviors, targeted by most interventions. Research priorities include measuring social factors better, monitoring social factors and health relative to policies, examining health effects of social factors across lifetimes and generations, incrementally elucidating pathways through knowledge linkage, testing multidimensional interventions, and addressing political will as a key barrier to translating knowledge into action.
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            Implicit bias in healthcare professionals: a systematic review

            Background Implicit biases involve associations outside conscious awareness that lead to a negative evaluation of a person on the basis of irrelevant characteristics such as race or gender. This review examines the evidence that healthcare professionals display implicit biases towards patients. Methods PubMed, PsychINFO, PsychARTICLE and CINAHL were searched for peer-reviewed articles published between 1st March 2003 and 31st March 2013. Two reviewers assessed the eligibility of the identified papers based on precise content and quality criteria. The references of eligible papers were examined to identify further eligible studies. Results Forty two articles were identified as eligible. Seventeen used an implicit measure (Implicit Association Test in fifteen and subliminal priming in two), to test the biases of healthcare professionals. Twenty five articles employed a between-subjects design, using vignettes to examine the influence of patient characteristics on healthcare professionals’ attitudes, diagnoses, and treatment decisions. The second method was included although it does not isolate implicit attitudes because it is recognised by psychologists who specialise in implicit cognition as a way of detecting the possible presence of implicit bias. Twenty seven studies examined racial/ethnic biases; ten other biases were investigated, including gender, age and weight. Thirty five articles found evidence of implicit bias in healthcare professionals; all the studies that investigated correlations found a significant positive relationship between level of implicit bias and lower quality of care. Discussion The evidence indicates that healthcare professionals exhibit the same levels of implicit bias as the wider population. The interactions between multiple patient characteristics and between healthcare professional and patient characteristics reveal the complexity of the phenomenon of implicit bias and its influence on clinician-patient interaction. The most convincing studies from our review are those that combine the IAT and a method measuring the quality of treatment in the actual world. Correlational evidence indicates that biases are likely to influence diagnosis and treatment decisions and levels of care in some circumstances and need to be further investigated. Our review also indicates that there may sometimes be a gap between the norm of impartiality and the extent to which it is embraced by healthcare professionals for some of the tested characteristics. Conclusions Our findings highlight the need for the healthcare profession to address the role of implicit biases in disparities in healthcare. More research in actual care settings and a greater homogeneity in methods employed to test implicit biases in healthcare is needed.
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              Inequality and the health-care system in the USA.

              Widening economic inequality in the USA has been accompanied by increasing disparities in health outcomes. The life expectancy of the wealthiest Americans now exceeds that of the poorest by 10-15 years. This report, part of a Series on health and inequality in the USA, focuses on how the health-care system, which could reduce income-based disparities in health, instead often exacerbates them. Other articles in this Series address population health inequalities, and the health effects of racism, mass incarceration, and the Affordable Care Act (ACA). Poor Americans have worse access to care than do wealthy Americans, partly because many remain uninsured despite coverage expansions since 2010 due to the ACA. For individuals with private insurance, rising premiums and cost sharing have undermined wage gains and driven many households into debt and even bankruptcy. Meanwhile, the share of health-care resources devoted to care of the wealthy has risen. Additional reforms that move forward, rather than backward, from the ACA are sorely needed to mitigate health and health-care inequalities and reduce the financial burdens of medical care borne by non-wealthy Americans.
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                Author and article information

                Journal
                Journal of General Internal Medicine
                J GEN INTERN MED
                Springer Science and Business Media LLC
                0884-8734
                1525-1497
                May 2019
                April 16 2019
                May 2019
                : 34
                : 5
                : 720-730
                Article
                10.1007/s11606-019-04876-0
                6502919
                30993619
                1780c2e6-a796-498f-93a6-98b0feb2ec4d
                © 2019

                http://www.springer.com/tdm

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