The monsoon wedding phenomenon: understanding Indian students studying in Australian universities

How does the University sector identify and support the diverse needs of Indian students? This paper reports on a research project carried out on undergraduate students from India enrolled at a Melbourne‐based University. The focus is the need to understand why Indian students choose an overseas destination for tertiary study. The intent is to explore how the curriculum that they have experienced in their country prepares them for study in another. We examine the expectations of students in relation to studying overseas. The suggestion underlying this paper is that if academic and support staff in tertiary education understand international students in cultural cohorts, then it is more likely that their transition to tertiary education will be easier. We envisage that this may also lead to a greater retention rate for universities.

Cognitive-reminiscence therapy and usual care for depression in young adults: study protocol for a randomized controlled trial

Background
Depression is a common affliction for young adults, and is associated with a range of adverse outcomes. Cognitive-reminiscence therapy is a brief, structured intervention that has been shown to be highly effective for reducing depressive symptoms, yet to date has not been evaluated in young adult populations. Given its basis in theory-guided reminiscence-based therapy, and incorporation of effective therapeutic techniques drawn from cognitive therapy and problem-solving frameworks, it is hypothesized to be effective in treating depression in this age group.

Methods and design
This article presents the design of a randomized controlled trial implemented in a community-based youth mental health service to compare cognitive-reminiscence therapy with usual care for the treatment of depressive symptoms in young adults. Participants in the cognitive-reminiscence group will receive six sessions of weekly, individual psychotherapy, whilst participants in the usual-care group will receive support from the youth mental health service according to usual procedures. A between-within repeated-measures design will be used to evaluate changes in self-reported outcome measures of depressive symptoms, psychological wellbeing and anxiety across baseline, three weeks into the intervention, post-intervention, one month post-intervention and three months post-intervention. Interviews will also be conducted with participants from the cognitive-reminiscence group to collect information about their experience receiving the intervention, and the process underlying any changes that occur.

Discussion
This study will determine whether a therapeutic approach to depression that has been shown to be effective in older adult populations is also effective for young adults. The expected outcome of this study is the validation of a brief, evidence-based, manualized treatment for young adults with depressive symptoms.

Reminiscence-based therapies for depression: should they be used only with older adults?

Reminiscence-based therapies have been reliably evidenced to be an effective intervention for depression. However, to date, their use has been restricted primarily to older adults. This article reviews empirical findings related to the various functions of reminiscence and their correlates with mental health. Reminiscence-based interventions and their effectiveness are then reviewed, with a particular focus on recent evaluations of structured reminiscence-based therapies that utilize preexisting therapeutic frameworks for the treatment for depression. The exclusive use of reminiscence-based therapies with older adult populations is then challenged, and it is argued that these approaches may be useful for reducing depression symptomatology for young and middle-aged adults also. Considerations for the use of reminiscence-based therapies in these populations are discussed, and future directions for research are presented.

Job strain — attributable depression in a sample of working Australians: assessing the contribution to health inequalities

Background The broad aim of this study was to assess the contribution of job strain to mental health inequalities by (a) estimating the proportion of depression attributable to job strain (low control and high demand jobs), (b) assessing variation in attributable risk by occupational skill level, and (c) comparing numbers of job strain–attributable depression cases to numbers of compensated 'mental stress' claims. Methods Standard population attributable risk (PAR) methods were used to estimate the proportion of depression attributable to job strain. An adjusted Odds Ratio (OR) of 1.82 for job strain in relation to depression was obtained from a recently published meta-analysis and combined with exposure prevalence data from the Australian state of Victoria. Job strain exposure prevalence was determined from a 2003 population-based telephone survey of working Victorians (n = 1101, 66% response rate) using validated measures of job control (9 items, Cronbach's alpha = 0.80) and psychological demands (3 items, Cronbach's alpha = 0.66). Estimates of absolute numbers of prevalent cases of depression and successful stress-related workers' compensation claims were obtained from publicly available Australian government sources. Results Overall job strain-population attributable risk (PAR) for depression was 13.2% for males [95% CI 1.1, 28.1] and 17.2% [95% CI 1.5, 34.9] for females. There was a clear gradient of increasing PAR with decreasing occupational skill level. Estimation of job strain–attributable cases (21,437) versus "mental stress" compensation claims (696) suggest that claims statistics underestimate job strain–attributable depression by roughly 30-fold. Conclusion Job strain and associated depression risks represent a substantial, preventable, and inequitably distributed public health problem. The social patterning of job strain-attributable depression parallels the social patterning of mental illness, suggesting that job strain is an important contributor to mental health inequalities. The numbers of compensated 'mental stress' claims compared to job strain-attributable depression cases suggest that there is substantial under-recognition and under-compensation of job strain-attributable depression. Primary, secondary, and tertiary intervention efforts should be substantially expanded, with intervention priorities based on hazard and associated health outcome data as an essential complement to claims statistics.

An empirical comparison of classification algorithms for diagnosis of depression from brain sMRI scans

To be diagnostically effective, structural magnetic resonance imaging (sMRI) must reliably distinguish a depressed individual from a healthy individual at individual scans level. One of the tasks in the automated diagnosis of depression from brain sMRI is the classification. It determines the class to which a sample belongs (i.e., depressed/not depressed, remitted/not-remitted depression) based on the values of its features. Thus far, very limited works have been reported for identification of a suitable classification algorithm for depression detection. In this paper, different types of classification algorithms are compared for effective diagnosis of depression. Ten independent classification schemas are applied and a comparative study is carried out. The algorithms are: Naïve Bayes, Support Vector Machines (SVM) with Radial Basis Function (RBF), SVM Sigmoid, J48, Random Forest, Random Tree, Voting Feature Intervals (VFI), LogitBoost, Simple KMeans Classification Via Clustering (KMeans) and Classification Via Clustering Expectation Minimization (EM) respectively. The performances of the algorithms are determined through a set of experiments on sMRI brain scans. An experimental procedure is developed to measure the performance of the tested algorithms. A classification accuracy evaluation method was employed for evaluation and comparison of the performance of the examined classifiers.

Investigating machine learning techniques for detection of depression using structural MRI volumetric features

Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression.

Depression, anxiety and metabolic syndrome in farm men and women

This research found that Depression was associated with the development of metabolic syndrome, whilst both Depression and Anxiety are associated with the maintenance of metabolic syndrome in Farm men and women. Future interventions in metabolic syndrome should consider screening for and treating these psychological factors to improve health outcomes.