Maintaining family stability in the age of digital technologies: An analysis of d/Discourse informing domestic screen media practices in three US families

This article examines d/Discourses about screen media and parenting as reflected in parents' discursive construction of their decision-making processes connected with domestic media practices. The analysis focuses on three parent interviews from a dataset of 51 parent interviews conducted in three states in the USA. The article illustrates ways d/Discourses connected with health, education, ethnicity and disability that are specific to these three households intersect with d/Discourses concerning children and media. The article argues that general categories describing parenting practices overlook the microcultures of households, erasing the emotional labour embedded in processes of decision-making.     

‘It is like being put through a blender’: inclusive research in practice in an Australian university

Abstract

How can university-based researchers committed to a position of solidarity with, and activism alongside, people with disabilities maintain such a stance in the metric-driven environment of the modern university? How can the academy ensure there is the opportunity for people with disabilities to contribute to production of the knowledge in which they have most at stake, in a wider environment where access to basic services for people with disabilities is precarious? In this article we draw on our experience as a team of university-based and community-based researchers with and without disabilities to reflect on these questions, using a framework of reflexive solidarity to consider practical strategies for strengthening the relationship between disability activism and the academy.     

Monotonic Bayesian Semiparametric Benchmark Dose Analysis

Quantitative risk assessment proceeds by first estimating a dose‐response model and then inverting this model to estimate the dose that corresponds to some prespecified level of response. The parametric form of the dose‐response model often plays a large role in determining this dose. Consequently, the choice of the proper model is a major source of uncertainty when estimating such endpoints. While methods exist that attempt to incorporate the uncertainty by forming an estimate based upon all models considered, such methods may fail when the true model is on the edge of the space of models considered and cannot be formed from a weighted sum of constituent models. We propose a semiparametric model for dose‐response data as well as deriving a dose estimate associated with a particular response. In this model formulation, the only restriction on the model form is that it is monotonic. We use this model to estimate the dose‐response curve from a long‐term cancer bioassay, as well as compare this to methods currently used to account for model uncertainty. A small simulation study is conducted showing that the method is superior to model averaging when estimating exposure that arises from a quantal‐linear dose‐response mechanism, and is similar to these methods when investigating nonlinear dose‐response patterns.     

Bayesian Quantile Impairment Threshold Benchmark Dose Estimation for Continuous Endpoints

Quantitative risk assessment often begins with an estimate of the exposure or dose associated with a particular risk level from which exposure levels posing low risk to populations can be extrapolated. For continuous exposures, this value, the benchmark dose, is often defined by a specified increase (or decrease) from the median or mean response at no exposure. This method of calculating the benchmark dose does not take into account the response distribution and, consequently, cannot be interpreted based upon probability statements of the target population. We investigate quantile regression as an alternative to the use of the median or mean regression. By defining the dose–response quantile relationship and an impairment threshold, we specify a benchmark dose as the dose associated with a specified probability that the population will have a response equal to or more extreme than the specified impairment threshold. In addition, in an effort to minimize model uncertainty, we use Bayesian monotonic semiparametric regression to define the exposure–response quantile relationship, which gives the model flexibility to estimate the quantal dose–response function. We describe this methodology and apply it to both epidemiology and toxicology data.     

Will boys’ mental health fare worse under a hotter climate in Australia?

A hotter climate is increasingly found to have negative effects on human health, yet the possible impact on children’s mental health is less understood. Our study explored this potential relationship using a national survey of children aged 6–11 across Australia, during the period 2008–2014 (n?=?14,096). It was hypothesised that the negative effects of hotter weather on children’s mental health can occur both directly (e.g. through heat impacts influencing hyperactivity and restlessness) and indirectly (e.g. through reduced participation in organised physical activities). Mediation analysis controlled for a range of other locational, gender, socio-economic and demographic influences. Results indicate that an increase in annual average daily maximum temperature worsened childhood mental health due to a direct and indirect effect through reduced participation in organised physical activities, as measured by the Strengths and Difficulties Questionnaire (SDQ) total score, but the result is only significant for boys (albeit the magnitude of the effect was small). More specifically, temperature differences are driven through the SDQ mental health sub-scales of hyperactivity and peer problems. Gender disparities are also observed in terms of other environmental or locational influences having a significant impact on boys’ mental health, with no significant impact found for girls. Girls’ mental health is more likely influenced by family and individual socio-economic characteristics. There is also evidence of an increased impact of higher temperature on children’s mental health in poorer households, suggesting the need for more targeted children’s mental health policies.     

Increasing Foreign Aid for Inclusive Human Development in Africa

In the light of evidence that poverty has been decreasing in all regions of the world with the exception of Africa, where about 45% of countries in sub-Saharan Africa did not achieve the Millennium development goal extreme poverty target, this study assesses whether increasing foreign aid improves inclusive human development. The investigation is on 53 African countries for the period 2005–2012. The empirical analysis is based on (1) the generalised method of moments (GMM) to control for persistence in inclusive human development, simultaneity and time-invariant omitted variables and (2) Instrumental Variable Tobit Regressions to control for simultaneity and the limited range in the dependent variable. The adopted foreign aid variables are: ‘humanitarian assistance’, ‘action on debt’ ‘aid for social infrastructure’, ‘aid to the productive sector’, ‘aid to the multi sector’, ‘aid for economic infrastructure’ and ‘programme assistance’. The following findings are established. From the GMM specifications, there are (1) synergy effects from ‘aid to the productive sector’ and a positive net effect from ‘programme assistance’ and (2) negative net impacts from ‘aid to social infrastructure’ and human assistance, albeit with positive marginal effects. From Instrumental Variable Tobit regressions (1) there is a synergy effect from ‘aid for economic infrastructure’ and (2) there are negative net impacts from ‘aid for social infrastructure’, ‘aid to the productive sector’ and human assistance, albeit with positive marginal effects. Policy implications are discussed.     

Bayesian Stacked Parametric Survival with Frailty Components and Interval‐Censored Failure Times: An Application to Food Allergy Risk

To better understand the risk of exposure to food allergens, food challenge studies are designed to slowly increase the dose of an allergen delivered to allergic individuals until an objective reaction occurs. These dose‐to‐failure studies are used to determine acceptable intake levels and are analyzed using parametric failure time models. Though these models can provide estimates of the survival curve and risk, their parametric form may misrepresent the survival function for doses of interest. Different models that describe the data similarly may produce different dose‐to‐failure estimates. Motivated by predictive inference, we developed a Bayesian approach to combine survival estimates based on posterior predictive stacking, where the weights are formed to maximize posterior predictive accuracy. The approach defines a model space that is much larger than traditional parametric failure time modeling approaches. In our case, we use the approach to include random effects accounting for frailty components. The methodology is investigated in simulation, and is used to estimate allergic population eliciting doses for multiple food allergens.