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.     

The combined relations of gender,enculturation, and depressive symptoms with health risk behaviors in Mexican-Americans: a moderated mediation analysis

ABSTRACT

Objectives: The present study investigated the relationships of enculturation and depressive symptoms with health risk behavior engagement in Mexican-American college students and examined how these relationships differed by gender. Previous research has noted consistent gender differences in health risk behavior (e.g. alcohol use, substance use, and risky sexual behavior) among Latina/os, and emphasized the role of U.S. acculturation in this difference. Research examining the role of heritage cultural retention (i.e. enculturation), and including the added influence of mental health variables, such as depressive symptoms, is currently lacking. This study sought to address this gap.

Design: A large sample (N?=?677) of Mexican-American college students from four universities (located in New York, California, Florida, and Texas) completed an online questionnaire assessing health risk behaviors and corresponding variables.

Results: We found that males who endorsed more behavioral enculturation and depressive symptoms were more likely to engage in health risk behavior than all others in the sample. Contrary to previous literature, no relationship was found between behavioral enculturation and health risk behavior in females.

Conclusion: The current study found behavioral enculturation to be associated with depressive symptoms, and in turn with health risk behaviors among the males in our sample. Additional research will be needed to identify the mechanism underlying the relationship between enculturation and depressive symptoms as well as between depressive symptoms and risky behavior.     

The technological imperative in tuberculosis care and prevention in Vietnam

ABSTRACT

A monocausal bacteriological understanding of infectious disease orients tuberculosis control efforts towards antimicrobial interventions. A bias towards technological solutions can leave multistranded public health and social interventions largely neglected. In the context of globalising biomedical approaches to infectious disease control, this ethnography-inspired review article reflects upon the implementation of rapid diagnostic technology in low- and middle-income countries. Fieldwork observations in Vietnam provided a stimulus for a critical review of the global rollout of tuberculosis diagnostic technology. To address local needs in tuberculosis control, health managers in resource-poor settings are readily cooperating with international donors to deploy novel diagnostic technologies throughout national tuberculosis programme facilities. Increasing investment in new diagnostic technologies is predicated on the supposition that these interventions will ameliorate disease outcomes. However, suboptimal treatment control persists even when accurate diagnostic technologies are available, suggesting that promotion of singular technological solutions can distract from addressing systemic change, without which disease susceptibility, propagation of infection, detection gaps, diagnostic delays, and treatment shortfalls persist.     

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.