Indonesia's national parties and the representation of a diverse society

 This study explores laws that promote the nationalisation of Indonesia’s political parties, and considers what this means for the representation of a diverse society. Overall, the research finds that the laws have restricted the development of political parties, but not for the reasons commonly expected.

A qualitative exploration of body image experiences of women progressing through pregnancy

BACKGROUND: Pregnancy provides an interesting challenge to body image theories in that the natural physiological changes push women further from the socioculturally prescribed thin ideal which these theories hinge upon. The impact that these significant physiological changes have on the woman's body image during pregnancy may depend on the extent to which they retain or revise the ideal. However, little is known about body image experiences during pregnancy. AIM: To provide a comprehensive exploration of the body image experiences of pregnant women. METHODS: Individual structured interviews were conducted with 19 currently pregnant women. Transcriptions were analysed using a thematic content analysis approach. FINDINGS: Themes extracted from the qualitative data included: (1) women's body image experiences during pregnancy were complex and changing, and shaped by the salience of specific body parts, the women's expectations for future changes to their body within the perinatal period, the functionality of the body, and their experience of maternity clothing, (2) women were able to negotiate the changes to their bodies as they recognised the functionality of the pregnant body, (3) women were surprised by the public nature of the pregnant body, (4) partner support and positive feedback about the pregnant body was highly valued, and (5) the importance of open communication around weight and body image in antenatal healthcare. DISCUSSION: Our findings highlight the need for the adaptation and expansion of existing body image theories to be used as a framework for women's experiences of pregnancy.

Views of women and health professionals on mHealth lifestyle interventions in pregnancy: a qualitative investigation

BACKGROUND: Evidence suggests that women are failing to meet guidelines for nutrition, physical activity, and weight gain during pregnancy. Interventions to promote a healthy lifestyle in pregnancy demonstrate mixed results and many are time and resource intensive. mHealth-delivered interventions offer an opportunity to provide trusted source information in a timely and cost-effective manner. Studies regarding women's and health professionals' views of mHealth in antenatal care are limited.

OBJECTIVE: This study aimed to explore women's and health professionals' views regarding mHealth information sources and interventions to assist women to eat well, be physically active, and gain healthy amounts of weight in pregnancy.

METHODS: A descriptive qualitative research approach employed focus groups and in-depth interviews with 15 pregnant or postpartum women and 12 in-depth interviews with health professionals including two from each category: obstetricians, general practitioners, midwives, dietitians, physiotherapists, and community pharmacists. All interviews were transcribed verbatim and thematically analyzed.

RESULTS: Women uniformly embraced the concept of mHealth information sources and interventions in antenatal care and saw them as central to information acquisition and ideally incorporated into future antenatal care processes. Health professionals exhibited varied views perceiving mHealth as an inevitable, often parallel, service rather than one integrated into the care model. Four key themes emerged: engagement, risk perception, responsibility, and functionality. Women saw their ability to access mHealth elements as a way to self-manage or control information acquisition that was unavailable in traditional care models and information sources. The emergence of technology was perceived by some health professionals to have shifted control of information from trusted sources, such as health professionals and health organizations, to nontrusted sources. Some health professionals were concerned about the medicolegal risks of mHealth (incorrect or harmful information and privacy concerns), while others acknowledged that mHealth was feasible if inherent risks were addressed. Across both groups, there was uncertainty as to who should be responsible for ensuring high-quality mHealth. The absence of a key pregnancy or women's advocacy group, lack of health funds for technologies, and the perceived inability of maternity hospitals to embrace technology were seen to be key barriers to provision. Women consistently identified the functionality of mHealth as adding value to antenatal care models. For some health professionals, lack of familiarity with and fear of mHealth limited their engagement with and comprehension of the capacity of new technologies to support antenatal care.

CONCLUSIONS: Women exhibited positive views regarding mHealth for the promotion of a healthy lifestyle in antenatal care. Conversely, health professionals expressed a much wider variation in attitudes and were more able to identify potential risks and barriers to development and implementation. This study contributes to the understanding of the opportunities and challenges in developing mHealth lifestyle interventions in antenatal care.

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.