Part-Time Work and the Gender Division of Labor

In response to the incompatible demands of work and motherhood, many mothers seek out part-time work schedules. Although many mothers consider this option “the best of both worlds,” scholars are divided about whether part-time work is in women’s best interest because it is linked to the gender division of labor in the home, and hence, to gender inequality. In this paper, we investigate the mostly unintended consequences of part-time work on the gender division of labor within the household. Drawing on 54 in-depth interviews with mothers who voluntarily work part-time, we explore how mothers experience household work and child care arrangements when they work part-time. Three factors emerged as most important in understanding how part-time work can shape mothers’ experiences of the gender division of labor: pathway to part-time work, work location, and work schedule. Depending on these factors, part-time work may be experienced as either enhancing or undermining of the gender division of labor, and thus, as promoting or undermining gender equality in their families.

The self-prescribed use of aromatherapy oils by pregnant women

BackgroundWhile some studies have reported effectiveness of aromatherapy oils use during labour there is no reported evidence of efficacy or risks of aromatherapy oils use for pregnancy-related symptoms or conditions. A number of aromatherapy oils are unsafe for use by pregnant women yet there is currently no research examining the prevalence and characteristics of women who use aromatherapy oils during pregnancy.AimTo conduct an empirical study of the prevalence and characteristics of women who use aromatherapy oils during pregnancy.MethodsThe research was conducted as part of the Australian Longitudinal Study on Women's Health (ALSWH), focusing on the nationally representative sample of Australian women aged 31–36 years. Data were collected via a cross-sectional questionnaire (n = 8200) conducted in 2009.ResultsSelf-prescribed aromatherapy oils were used by 15.2% of pregnant women. Pregnant women were 1.57 (95% CI: 1.01, 2.43) times more likely to self-prescribe use of aromatherapy oils if they have allergies or hayfever, and 2.26 (95% CI: 1.34, 3.79) times more likely to self-prescribe use of aromatherapy oils if they have a urinary tract infection (UTI).ConclusionOur study highlights a considerable use of aromatherapy oils by pregnant women. There is a clear need for greater communication between practitioners and patients regarding the use of aromatherapy oils during pregnancy, as well a need for health care practitioners to be mindful that pregnant women in their care may be using aromatherapy oils, some of which may be unsafe.     

European Federation of Statisticians in the Pharmaceutical Industry's position on access to clinical trial data

The European Federation of Statisticians in the Pharmaceutical Industry (EFSPI) believes access to clinical trial data should be implemented in a way that supports good research, avoids misuse of such data, lies within the scope of the original informed consent and fully protects patient confidentiality. In principle, EFSPI supports responsible data sharing. EFSPI acknowledges it is in the interest of patients that their data are handled in a strictly confidential manner to avoid misuse under all possible circumstances. It is also in the interest of the altruistic nature of patients participating in trials that such data will be used for further development of science as much as possible applying good statistical principles. This paper summarises EFSPI's position on access to clinical trial data. The position was developed during the European Medicines Agency (EMA) advisory process and before the draft EMA policy on publication and access to clinical trial data was released for consultation; however, the EFSPI's position remains unchanged following the release of the draft policy. Finally, EFSPI supports a need for further guidance to be provided on important technical aspects relating to re‐analyses and additional analyses of clinical trial data, for example, multiplicity, meta‐analysis, subgroup analyses and publication bias. Copyright © 2013 John Wiley & Sons, Ltd.     

The analysis of mixed and random effects models for equireplicate variance-balanced block designs

Equirephcate variance-balanced block designs possess many desirable properties and include several important subclasses of designs, such as balanced incomplete block designs. However, while attention has been paid to the derivation of properties and the construction of these designs, methods of analyzing experiments using these designs have not been developed completely. Thus, we consider models appropriate to these designs, construct test statistics for basic hypotheses and derive the distributions for these statistics. Four additive models that include all of the possible combinations of fixed and random effects are examined. The analyses are generalizations of the analyses for balanced incomplete block designs, and include the intra-block, intra-treatment and inter-block analyses, Estimators of fixed treatment effects and their distributions also are given.     

Single subject research methods in rehabilitation

The article describes single-subject research designs as an efficient and cost-effective way to assess the impact of targeted interventions on individual behavior. The authors overview the basic tenets of single-subject research, provide examples of several types of single-subject designs, and describe the utility of single-subject designs in rehabilitation research.     

A Bootstrap Mechanism for Response Masking in Remote Analysis Systems

National Statistical Agencies and other data custodian agencies hold a wealth of data regarding individuals and organizations, collected from censuses, surveys and administrative sources. In many cases, these data are made available to external researchers, for the investigation of questions of social and economic importance. To enhance access to this information, several national statistical agencies are developing remote analysis systems (RAS) designed to accept queries from a researcher, run them on data held in a secure environment, and then return the results. RAS prevent a researcher from accessing the underlying data, and most rely on manual checking to ensure the responses have acceptably low disclosure risk. However, the need for scalability and consistency will increasingly require automated methods. We propose a RAS output confidentialization procedure based on statistical bootstrapping that automates disclosure control while achieving a provably good balance between disclosure risk and usefulness of the responses. The bootstrap masking mechanism is easy to implement for most statistical queries, yet the characteristics of the bootstrap distribution assure us that it is also effective in providing both useful responses and low disclosure risk. Interestingly, our proposed bootstrap masking mechanism represents an ideal application of Efron's bootstrap—one that takes advantage of all the theoretical properties of the bootstrap, without ever having to construct the bootstrap distribution.