Job stress as a preventable upstream determinant of common mental disorders: a review for practitioners and policy-makers

There is growing recognition of the important role of mental health in the workforce and in the workplace. At the same time, there has been a rapid growth of studies linking job stress and other psychosocial working conditions to common mental disorders, and a corresponding increase in public concern media attention to job stress and its impact upon worker health and well-being. This article provides a summary of the relevant scientific and medical literature on this topic for practitioners and policy-makers. It presents a primer on job stress concepts, an overview of the evidence linking job stress and common mental disorders, a summary of the intervention research on ways to prevent and control job stress, and a discussion of the strengths and weakness of the evidence base. We conclude that there is strong evidence linking job stress and common mental disorders, and that it is a substantial problem on the population level. On a positive note, however, the job stress intervention evidence also shows that the problem is preventable and can be effectively addressed by a combination of work- and worker-directed intervention.

Sex as a determinant of relapse incidence and progressive course of multiple sclerosis

The aim of this work was to evaluate sex differences in the incidence of multiple sclerosis relapses; assess the relationship between sex and primary progressive disease course; and compare effects of age and disease duration on relapse incidence. Annualized relapse rates were calculated using the MSBase registry. Patients with incomplete data or <1 year of follow-up were excluded. Patients with primary progressive multiple sclerosis were only included in the sex ratio analysis. Relapse incidences over 40 years of multiple sclerosis or 70 years of age were compared between females and males with Andersen-Gill and Tweedie models. Female-to-male ratios stratified by annual relapse count were evaluated across disease duration and patient age and compared between relapse-onset and primary progressive multiple sclerosis. The study cohort consisted of 11 570 eligible patients with relapse-onset and 881 patients with primary progressive multiple sclerosis. Among the relapse-onset patients (82 552 patient-years), 48 362 relapses were recorded. Relapse frequency was 17.7% higher in females compared with males. Within the initial 5 years, the female-to-male ratio increased from 2.3:1 to 3.3:1 in patients with 0 versus ≥4 relapses per year, respectively. The magnitude of this sex effect increased at longer disease duration and older age (P < 10−12). However, the female-to-male ratio in patients with relapse-onset multiple sclerosis and zero relapses in any given year was double that of the patients with primary progressive multiple sclerosis. Patient age was a more important determinant of decline in relapse incidence than disease duration (P < 10−12). Females are predisposed to higher relapse activity than males. However, this difference does not explain the markedly lower female-to-male sex ratio in primary progressive multiple sclerosis. Decline in relapse activity over time is more closely related to patient age than disease duration.

Iterative development of Stand Up Australia : a multi-component intervention to reduce workplace sitting

Sitting, particularly in prolonged, unbroken bouts, is widespread within the office workplace, yet few interventions have addressed this newly-identified health risk behaviour. This paper describes the iterative development process and resulting intervention procedures for the Stand Up Australia research program focusing on a multi-component workplace intervention to reduce sitting time. The development of Stand Up Australia followed three phases. 1) Conceptualisation: Stand Up Australia was based on social cognitive theory and social ecological model components. These were operationalised via a taxonomy of intervention strategies and designed to target multiple levels of influence including: organisational structures (e.g. via management consultation), the physical work environment (via provision of height-adjustable workstations), and individual employees (e.g. via face-to-face coaching). 2) Formative research: Intervention components were separately tested for their feasibility and acceptability. 3) Pilot studies: Stand Up Comcare tested the integrated intervention elements in a controlled pilot study examining efficacy, feasibility and acceptability. Stand Up UQ examined the additional value of the organisational- and individual-level components over height-adjustable workstations only in a three-arm controlled trial. In both pilot studies, office workers’ sitting time was measured objectively using activPAL3 devices and the intervention was refined based on qualitative feedback from managers and employees. Results and feedback from participants and managers involved in the intervention development phases suggest high efficacy, acceptance, and feasibility of all intervention components. The final version of the Stand Up Australia intervention includes strategies at the organisational (senior management consultation, representatives consultation workshop, team champions, staff information and brainstorming session with information booklet, and supportive emails from managers to staff), environmental (height-adjustable workstations), and individual level (face-to-face coaching session and telephone support). Stand Up Australia is currently being evaluated in the context of a cluster-randomised controlled trial at the Department of Human Services (DHS) in Melbourne, Australia. Stand Up Australia is an evidence-guided and systematically developed workplace intervention targeting reductions in office workers’ sitting time.

Learning user preference patterns for Top-N recommendations

In this paper, we observe that the user preference styles tend to change regularly following certain patterns. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation- Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N recommendation in terms of accuracy. © 2012 IEEE.

Iterative construction of hierarchical classifiers for phishing website detection

This article is devoted to a new iterative construction of hierarchical classifiers in SimpleCLI for the detection of phishing websites. Our new construction of hierarchical systems creates ensembles of ensembles in SimpleCLI by iteratively linking a top-level ensemble to another middle-level ensemble instead of a base classifier so that the top-level ensemble can generate a large multilevel system. This new construction makes it easy to set up and run such large systems in SimpleCLI. The present article concentrates on the investigation of performance of the iterative construction of such classifiers for the example of detection of phishing websites. We carried out systematic experiments evaluating several essential ensemble techniques as well as more recent approaches and studying their performance as parts of the iterative construction of hierarchical classifiers. The results presented here demonstrate that the iterative construction of hierarchical classifiers performed better than the base classifiers and standard ensembles. This example of application to the classification of phishing websites shows that the new iterative construction combining diverse ensemble techniques into the iterative construction of hierarchical classifiers can be applied to increase the performance in situations where data can be processed on a large computer. © 2014 ACADEMY PUBLISHER.

Large iterative multitier ensemble classifiers for security of big data

This paper introduces and investigates large iterative multitier ensemble (LIME) classifiers specifically tailored for big data. These classifiers are very large, but are quite easy to generate and use. They can be so large that it makes sense to use them only for big data. They are generated automatically as a result of several iterations in applying ensemble meta classifiers. They incorporate diverse ensemble meta classifiers into several tiers simultaneously and combine them into one automatically generated iterative system so that many ensemble meta classifiers function as integral parts of other ensemble meta classifiers at higher tiers. In this paper, we carry out a comprehensive investigation of the performance of LIME classifiers for a problem concerning security of big data. Our experiments compare LIME classifiers with various base classifiers and standard ordinary ensemble meta classifiers. The results obtained demonstrate that LIME classifiers can significantly increase the accuracy of classifications. LIME classifiers performed better than the base classifiers and standard ensemble meta classifiers.

Containing smartphone worm propagation with an influence maximization algorithm

In recent years, wide attention has been drawn to the problem of containing worm propagation in smartphones. Unlike existing containment models for worm propagation, we study how to prevent worm propagation through the immunization of key nodes (e.g.; the top k influential nodes). Thus, we propose a novel containment model based on an influence maximization algorithm. In this model, we introduce a social relation graph to evaluate the influence of nodes and an election mechanism to find the most influential nodes. Finally, this model provides a targeted immunization strategy to disable worm propagation by immunizing the top k influential nodes. The experimental results show that the model not only finds the most influential top k nodes quickly, but also effectively restrains and controls worm propagation.

Characterizing problematic hypoglycaemia: iterative design and preliminary psychometric validation of the Hypoglycaemia Awareness Questionnaire (HypoA-Q)

AIMS: To design and conduct preliminary validation of a measure of hypoglycaemia awareness and problematic hypoglycaemia, the Hypoglycaemia Awareness Questionnaire.

METHODS: Exploratory and cognitive debriefing interviews were conducted with 17 adults (nine of whom were women) with Type 1 diabetes (mean ± sd age 48±10 years). Questionnaire items were modified in consultation with diabetologists/psychologists. Psychometric validation was undertaken using data from 120 adults (53 women) with Type 1 diabetes (mean ± sd age 44±16 years; 50% with clinically diagnosed impaired awareness of hypoglycaemia), who completed the following questionnaires: the Hypoglycaemia Awareness Questionnaire, the Gold score, the Clarke questionnaire and the Problem Areas in Diabetes questionnaire.

RESULTS: Iterative design resulted in 33 items eliciting answers on awareness of hypoglycaemia when awake/asleep and hypoglycaemia frequency, severity and impact (healthcare utilization). Psychometric analysis identified three subscales reflecting 'impaired awareness', 'symptom level' and 'symptom frequency'. Convergent validity was indicated by strong correlations between the impaired awareness subscale and existing measures of awareness: (Gold: rs =0.75, P<0.01; Clarke: rs =0.76, P<0.01). Divergent validity was indicated by weaker correlations with diabetes-related distress (Problem Areas in Diabetes: rs =0.25, P<0.01) and HbA1c (rs =-0.05, non-significant). The impaired awareness subscale and other items discriminated between those with impaired and intact awareness (Gold score). The impaired awareness subscale and other items contributed significantly to models explaining the occurrence of severe hypoglycaemia and hypoglycaemia when asleep.

CONCLUSIONS: This preliminary validation shows the Hypoglycaemia Awareness Questionnaire has robust face and content validity; satisfactory structure; internal reliability; convergent, divergent and known groups validity. The impaired awareness subscale and other items contribute significantly to models explaining recall of severe and nocturnal hypoglycaemia. Prospective validation, including determination of a threshold to identify impaired awareness, is now warranted.

Habitat switching by Bewick's swans: Maximization of average long-term energy gain?

1. In a system where depletion drives a habitat shift, the hypothesis was tested that animals switch habitat as soon as the average daily net energy intake (or gain) drops below that attainable in the alternative habitat.

2. The study was performed in the Lauwersmeer area. Upon arrival during the autumn migration, Bewick's swans first feed on below-ground tubers of fennel pondweed on the lake, but subsequently switched to feeding on harvest remains in sugar beet fields.

3. The daily energy intake was estimated by multiplying the average time spent foraging per day with the instantaneous energy intake rate while foraging. In the case of pondweed feeding, the latter was estimated from the functional response and the depletion of tuber biomass. In the case of beet feeding, it was estimated from dropping production rate. Gross energy intake was converted to metabolizable energy intake using the assimilation as determined in digestion trials. The daily energy expenditure was estimated by the time-energy budget method. Energetic costs were determined using heart rate.

4. The daily gain of pondweed feeding at the median date of the habitat switch (i.e. when 50% of the swans had switched) was compared with that of beet feeding. The daily gain of beet feeding was calculated for two strategies depending on the night activity on the lake: additional pondweed feeding (mixed feeding) or sleeping (pure beet feeding).

5. The majority of the swans switched when the daily gain they could achieve by staying on the pondweed bed fell just below the average daily gain of pure beet feeders. However, mixed feeders would attain an average daily gain considerably above that of pondweed feeders. A sensitivity analysis showed that this result was robust.

6. We therefore reject the hypothesis that the habitat switch by swans can be explained by simple long-term energy rate maximization. State-dependency, predation risk, and protein requirements are put forward as explanations for the delay in habitat switch.

Modelling personal preferences for Top-N movie recommendations

Modelling the temporal dynamics of personal preferences is still under-developed despite the rapid development of personalization. In this paper, we observe that the user preference styles tend to change regularly following certain patterns in the context of movie recommendation systems. Therefore, we propose a Preference Pattern model to capture the user preference styles and their temporal dynamics, and apply this model to improve the accuracy of the Top-N movie recommendations. Precisely, a preference pattern is defined as a set of user preference styles sorted in a time order. The basic idea is to model user preference styles and their temporal dynamics by constructing a representative subspace with an Expectation-Maximization (EM)-like algorithm, which works in an iterative fashion by refining the global and the personal preference styles simultaneously. Then, the degree which the recommendations match the active user's preference styles, can be estimated by measuring its reconstruction error from its projection on the representative subspace. The experiment results indicate that the proposed model is robust to the data sparsity problem, and can significantly outperform the state-of-the-art algorithms on the Top-N movie recommendations in terms of accuracy.

Enhancing predictive accuracy of cardiac autonomic neuropathy using blood biochemistry features and iterative multitier ensembles

Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

Explore the hidden treasure in protein-protein interaction networks - an iterative model for predicting protein functions

Protein-protein interaction networks constructed by high throughput technologies provide opportunities for predicting protein functions. A lot of approaches and algorithms have been applied on PPI networks to predict functions of unannotated proteins over recent decades. However, most of existing algorithms and approaches do not consider unannotated proteins and their corresponding interactions in the prediction process. On the other hand, algorithms which make use of unannotated proteins have limited prediction performance. Moreover, current algorithms are usually one-off predictions. In this paper, we propose an iterative approach that utilizes unannotated proteins and their interactions in prediction. We conducted experiments to evaluate the performance and robustness of the proposed iterative approach. The iterative approach maximally improved the prediction performance by 50%-80% when there was a high proportion of unannotated neighborhood protein in the network. The iterative approach also showed robustness in various types of protein interaction network. Importantly, our iterative approach initially proposes an idea that iteratively incorporates the interaction information of unannotated proteins into the protein function prediction and can be applied on existing prediction algorithms to improve prediction performance.

Racism as a determinant of health: a systematic review and meta-analysis

Despite a growing body of epidemiological evidence in recent years documenting the health impacts of racism, the cumulative evidence base has yet to be synthesized in a comprehensive meta-analysis focused specifically on racism as a determinant of health. This meta-analysis reviewed the literature focusing on the relationship between reported racism and mental and physical health outcomes. Data from 293 studies reported in 333 articles published between 1983 and 2013, and conducted predominately in the U.S., were analysed using random effects models and mean weighted effect sizes. Racism was associated with poorer mental health (negative mental health: r = -.23, 95% CI [-.24,-.21], k = 227; positive mental health: r = -.13, 95% CI [-.16,-.10], k = 113), including depression, anxiety, psychological stress and various other outcomes. Racism was also associated with poorer general health (r = -.13 (95% CI [-.18,-.09], k = 30), and poorer physical health (r = -.09, 95% CI [-.12,-.06], k = 50). Moderation effects were found for some outcomes with regard to study and exposure characteristics. Effect sizes of racism on mental health were stronger in cross-sectional compared with longitudinal data and in non-representative samples compared with representative samples. Age, sex, birthplace and education level did not moderate the effects of racism on health. Ethnicity significantly moderated the effect of racism on negative mental health and physical health: the association between racism and negative mental health was significantly stronger for Asian American and Latino(a) American participants compared with African American participants, and the association between racism and physical health was significantly stronger for Latino(a) American participants compared with African American participants. Protocol PROSPERO registration number: CRD42013005464.