South African abortion attitudes from 2007-2016: the roles of religiosity and attitudes toward sexuality and gender equality

ABSTRACT

Abortion is legal in South Africa, but negative abortion attitudes remain common and are poorly understood. We used nationally representative South African Social Attitudes Survey data to analyze abortion attitudes in the case of fetal anomaly and in the case of poverty from 2007 to 2016 (n = 20,711; ages = 16+). We measured correlations between abortion attitudes and these important predictors: religiosity, attitudes about premarital sex, attitudes about preferential hiring and promotion of women, and attitudes toward family gender roles. Abortion acceptability for poverty increased over time (b = 0.05, p < .001), but not for fetal anomaly (b = ?0.008, p = .284). Highly religious South Africans reported lower abortion acceptability in both cases (Odds Ratio (OR)_anomaly = 0.85, p = .015; OR_poverty = 0.84, p = .02). Premarital sex acceptability strongly and positively predicted abortion acceptability (OR_anomaly = 2.63, p < .001; OR_poverty = 2.46, p < .001). Attitudes about preferential hiring and promotion of women were not associated with abortion attitudes, but favorable attitudes about working mothers were positively associated with abortion acceptability for fetal anomaly ((OR_anomaly = 1.09, p = .01; OR_poverty = 1.02, p = .641)). Results suggest negative abortion attitudes remain common in South Africa and are closely tied to religiosity, traditional ideologies about sexuality, and gender role expectations about motherhood.     

Humanitarianism from Below: Sowa Rigpa,the Traditional Pharmaceutical Industry,and Global Health

ABSTRACT

In this article I explore, for the first time, the relationship between Sowa Rigpa (Tibetan medicine) and global health, tracing “the global” in ethical discourses and pharmaceutical innovation practices of Tibetan medical practitioners. I argue that Sowa Rigpa’s engagement with the world and its global health activities outside China can be understood as a form of “humanitarianism from below,” while its industrialization in China aligns with global health in different ways. In providing new insights into recent developments of Sowa Rigpa, I aim to decenter the notion of humanitarianism and contribute to a broader understanding of global health.     

广东省家庭医生式服务试点地区居民健康行为及影响因素分析

目的了解广东省家庭医生式服务试点地区居民健康行为及影响因素,为开展健康教育提供依据。方法2015年7—10月抽样选取1010名家庭医生式服务试点地区居民进行问卷调查,以吸烟、饮酒、早餐、吃水果、锻炼及睡眠等6项行为为调查内容,同时选用P-CAT量表测量基层医疗服务质量。结果 6项行为方式共赋值12分,被调查者平均得分(8.72?0.65)分,得分从高到低依次为吃早餐、限酒、不吸烟、体育锻炼、吃水果和睡眠。多重线性回归分析结果显示:女性、已婚或慢性病患者的健康行为优于各对应组;收入对健康行为有正面影响;基层医疗服务可及性、服务提供综合性对健康行为有正向影响。结论试点地区居民整体健康行为较好。健康教育和健康促进应聚焦于合理膳食、锻炼及睡眠等健康行为;男性、单身或离异及低收入群体是重点关注的对象;提高基层医疗服务可及性和服务提供综合性有利于促进居民的健康行为。     

Rethinking PICO in the Machine Learning Era: ML-PICO

Background  Machine learning (ML) has captured the attention of many clinicians who may not have formal training in this area but are otherwise increasingly exposed to ML literature that may be relevant to their clinical specialties. ML papers that follow an outcomes-based research format can be assessed using clinical research appraisal frameworks such as PICO (Population, Intervention, Comparison, Outcome). However, the PICO frameworks strain when applied to ML papers that create new ML models, which are akin to diagnostic tests. There is a need for a new framework to help assess such papers. Objective  We propose a new framework to help clinicians systematically read and evaluate medical ML papers whose aim is to create a new ML model: ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes). We describe how the ML-PICO framework can be applied toward appraising literature describing ML models for health care. Conclusion  The relevance of ML to practitioners of clinical medicine is steadily increasing with a growing body of literature. Therefore, it is increasingly important for clinicians to be familiar with how to assess and best utilize these tools. In this paper we have described a practical framework on how to read ML papers that create a new ML model (or diagnostic test): ML-PICO. We hope that this can be used by clinicians to better evaluate the quality and utility of ML papers.     

Participatory research in Northwest Atlanta's Proctor Creek Watershed: Using photovoice to explore environmental health risks at the water's edge

In this study we used a participatory research method, photovoice, to explore community perceptions about environmental health risks, community assets, and strengths in and around an urban, degraded watershed in Northwest Atlanta, Georgia. This watershed, formed by Proctor Creek, is a focal point for redevelopment and infrastructure investments for years to come. Using a community-based participatory research approach, 10 Proctor Creek residents (watershed researchers), and a university partner, engaged in data collection; participatory data analysis; internal discussions; translation of research findings into watershed restoration, community revitalization, remedial action, and policy solutions; and dissemination of results to fellow watershed residents, stakeholders, and decision makers. We present a conceptual model linking the watershed researchers' understanding of urban policies and practice in the Proctor Creek Watershed to environmental, neighborhood and housing conditions and their influence on health outcomes and quality of life. Engaging community members in defining their own community environmental health challenges and assets yielded the following primary themes: 1) threats to the natural environment, 2) built environment stressors that influence health, 3) blight and divestment of public resources, and 4) hope for the future. Residents’ vision for the future of the watershed - a restored creek, revitalized neighborhoods, and restored people - is fueled by a strong connection to history, memory, and sense of place. We demonstrate the value of local knowledge in identifying previously unaddressed environmental health risks in the Proctor Creek Watershed as well as solutions to reduce or eliminate them.