Getting the complete picture : combining parental and child data to identify the barriers to social inclusion for children living in low socio-economic areas

Background: Childhood mental health problems are prevalent in Australian children (14–20%). Social exclusion is a risk factor for mental health problems, whereas being socially included can have protective effects. This study aims to identify the barriers to social inclusion for children aged 9–12 years living in low socio-economic status (SES) areas, using both child-report and parent-report interviews.

Methods: Australian-born English-speaking parents and children aged 9–12 years were sampled from a low SES area to participate in semi-structured interviews. Parents and children were asked questions around three prominent themes of social exclusion; exclusion from school, social activities and social networks.

Results: Many children experienced social exclusion at school, from social activities or within social networks. Overall, nine key barriers to social inclusion were identified through parent and child interviews, such as inability to attend school camps and participate in school activities, bullying and being left out, time and transport constraints, financial constraints and safety and traffic concerns. Parents and children often identified different barriers.

Discussion: There are several barriers to social inclusion for children living in low SES communities, many of which can be used to facilitate mental health promotion programmes. Given that parents and children may report different barriers, it is important to seek both perspectives.

Conclusion: This study strengthens the evidence base for the investments and action required to bring about the conditions for social inclusion for children living in low SES communities.

The mechanism of metadynamic softening in austenite after complete dynamic recrystallization

A novel mechanism of post-dynamic softening during annealing of a fully dynamically recrystallized (DRX) austenitic Ni–30Fe alloy is proposed. The initial softening stage involves rapid growth of the dynamically formed nuclei and migration of the mobile boundaries. The sub-boundaries within DRX grains progressively disintegrate through dislocation climb and dislocation annihilation, which ultimately leads to the formation of dislocation-free grains, and the grain boundary migration gradually becomes slower. As a result, the DRX texture largely remains preserved throughout the annealing process.

An input-output-based linkage measurement framework

This study proposed an input-output-based linkage measurement framework, which is a multi-level hierarchy, omni-direction decision-making model and takes the impact of capital into account. This framework had resolved the critical deficiencies and inherent limitations of the existing methods and was used to explore the real estate and construction linkages.

Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model

When using linguistic approaches to solve decision problems, we need the techniques for computing with words (CW). Together with the 2-tuple fuzzy linguistic representation models (i.e., the Herrera and Mart´ınez model and the Wang and Hao model), some computational techniques for CW are also developed. In this paper, we define the concept of numerical scale and extend the 2-tuple fuzzy linguistic representation models under the numerical scale.We find that the key of computational techniques
based on linguistic 2-tuples is to set suitable numerical scale with
the purpose of making transformations between linguistic 2-tuples
and numerical values. By defining the concept of the transitive
calibration matrix and its consistent index, this paper develops an optimization model to compute the numerical scale of the linguistic term set. The desired properties of the optimization model are also presented. Furthermore, we discuss how to construct the transitive calibration matrix for decision problems using linguistic preference relations and analyze the linkage between the consistent index of the transitive calibration matrix and one of the linguistic preference relations. The results in this paper are pretty helpful to complete the fuzzy 2-tuple representation models for CW.

Corporate commitment to sustainability – is it all hot air? A review of the linkage between executive pay and sustainable performance

Across time, companies are increasingly making public commitments to sustainable development and to reducing their impacts on climate change. Management remuneration plans (MRPs) are a key mechanism to motivate managers to achieve corporate goals. We review the MRPs negotiated with key management personnel in a sample of large Australian carbon-intensive companies. Our results show that, as in past decades, the companies in our sample have MRPs in place that continue to fixate on financial performance. We argue that this provides evidence of a disconnection between the sustainability-related rhetoric of the sample companies, and their ‘real’ organisational priorities.

Optimal radio labellings of complete m-ary trees

A radio labelling of a connected graph G is a mapping f : V (G) → {0, 1, 2, ...} such that | f (u) - f (v) | ≥ diam (G) - d (u, v) + 1 for each pair of distinct vertices u, v ∈ V (G), where diam (G) is the diameter of G and d (u, v) the distance between u and v. The span of f is defined as max_u, _{v ∈ V (G)} | f (u) - f (v) |, and the radio number of G is the minimum span of a radio labelling of G. A complete m-ary tree (m ≥ 2) is a rooted tree such that each vertex of degree greater than one has exactly m children and all degree-one vertices are of equal distance (height) to the root. In this paper we determine the radio number of the complete m-ary tree for any m ≥ 2 with any height and construct explicitly an optimal radio labelling.

Efficient cross-validation of the complete two stages in KFD classifier formulation

This paper presents an efficient evaluation algorithm for cross-validating the two-stage approach of KFD classifiers. The proposed algorithm is of the same complexity level as the existing indirect efficient cross-validation methods but it is more reliable since it is direct and constitutes exact cross-validation for the KFD classifier formulation. Simulations demonstrate that the proposed algorithm is almost as fast as the existing fast indirect evaluation algorithm and the twostage cross-validation selects better models on most of the thirteen benchmark data sets.

Validation of de-identified record linkage to ascertain hospital admissions in a cohort study

Background Cohort studies can provide valuable evidence of cause and effect relationships but are subject to loss of participants over time, limiting the validity of findings. Computerised record linkage offers a passive and ongoing method of obtaining health outcomes from existing routinely collected data sources. However, the quality of record linkage is reliant upon the availability and accuracy of common identifying variables. We sought to develop and validate a method for linking a cohort study to a state-wide hospital admissions dataset with limited availability of unique identifying variables.

Methods A sample of 2000 participants from a cohort study (n = 41 514) was linked to a state-wide hospitalisations dataset in Victoria, Australia using the national health insurance (Medicare) number and demographic data as identifying variables. Availability of the health insurance number was limited in both datasets; therefore linkage was undertaken both with and without use of this number and agreement tested between both algorithms. Sensitivity was calculated for a sub-sample of 101 participants with a hospital admission confirmed by medical record review.

Results Of the 2000 study participants, 85% were found to have a record in the hospitalisations dataset when the national health insurance number and sex were used as linkage variables and 92% when demographic details only were used. When agreement between the two methods was tested the disagreement fraction was 9%, mainly due to "false positive" links when demographic details only were used. A final algorithm that used multiple combinations of identifying variables resulted in a match proportion of 87%. Sensitivity of this final linkage was 95%.

Conclusions High quality record linkage of cohort data with a hospitalisations dataset that has limited identifiers can be achieved using combinations of a national health insurance number and demographic data as identifying variables.