There is increasing evidence that work-life imbalance has a direct impact on societal issues, such as delayed parenting, declining fertility rates, ageing populations, and decreasing labour supply. It is documented that work-life balance policies are beneficial for individuals, their families, organisations, and society. However, other evidence demonstrates that the associated benefits are not always realised and work-life balance policies can result in reinforced gender inequities and increased levels of work-life conflict. This paper reviews the ability of work-life balance policies to actually influence some key social and organisational issues. Current developments, such as an increased casual workforce and the impact of changes in newly industrialised nations, are discussed. Recommendations for work-life balance to be addressed via a comprehensive multilevel approach are made.

Stoma care nurses are in a unique position to offer support and advice to enable ostomates to achieve their optimum quality of life, allowing them to fulfil their family, work and social duties and return to the relationships, activities, sports, hobbies and lifestyle that they enjoyed before their surgery. Stoma care nurses use their expert knowledge and skills to offer such support, and have an in-depth knowledge of the appliances and accessories that can assist people to not only maintain healthy skin, but to feel confident and secure when wearing their pouch and allow them to function as independently and to achieve as much as possible. This article discusses some of the accessories that may be useful for those requiring additional support, to enable them to return to their usual lifestyle or enhance their quality of life.

Objective: To identify cognitive phenotypes in late-life depression (LLD) and describe relationships with sociodemographic and clinical characteristics. Design: Observational cohort study Setting: Baseline data from participants recruited via clinical referrals and community advertisements who enrolled in two separate studies. Participants: Non-demented adults with LLD (n = 120; mean age = 66.73 ± 5.35 years) and non-depressed elders (n = 56; mean age = 67.95 ± 6.34 years). Measurements: All completed a neuropsychological battery, and individual cognitive test scores were standardized across the entire sample without correcting for demographics. Five empirically derived cognitive domain composites were created, and cluster analytic approaches (hierarchical, k-means) were independently conducted to classify cognitive patterns in the depressed cohort only. Baseline sociodemographic and clinical characteristics were then compared across groups. Results: A three-cluster solution best reflected the data, including “High Normal” (n = 47), “Reduced Normal” (n = 35), and “Low Executive Function” (n = 37) groups. The “High Normal” group was younger, more educated, predominantly Caucasian, and had fewer vascular risk factors and higher Mini-Mental Status Examination compared to “Low Executive Function” group. No differences were observed on other sociodemographic or clinical characteristics. Exploration of the “High Normal” group found two subgroups that only differed in attention/working memory performance and length of the current depressive episode. Conclusions: Three cognitive phenotypes in LLD were identified that slightly differed in sociodemographic and disease-specific variables, but not in the quality of specific symptoms reported. Future work on these cognitive phenotypes will examine relationships to treatment response, vulnerability to cognitive decline, and neuroimaging markers to help disentangle the heterogeneity seen in this patient population

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Late-life depression (LLD) is characterized by accelerated biological aging. Accelerated brain aging, estimated from structural magnetic resonance imaging (sMRI) data by a machine learning algorithm, is associated with LLD diagnosis, poorer cognitive performance, and disability. We hypothesized that accelerated brain aging moderates the antidepressant response. Design and Interventions: Following MRI, participants entered an 8-week randomized, controlled trial of escitalopram. Nonremitting participants then entered an open-label 8-week trial of bupropion. Participants: Ninety-five individuals with LLD. Measurements: A machine learning algorithm estimated each participant's brain age from sMRI data. This was used to calculate the brain-age gap (BAG), or how estimated age differed from chronological age. Secondary sMRI measures of aging pathology included white matter hyperintensity (WMH) volumes and hippocampal volumes. Mixed models examined the relationship between sMRI measures and change in depression severity. Initial analyses tested for a moderating effect of MRI measures on change in depression severity with escitalopram. Subsequent analyses tested for the effect of MRI measures on change in depression severity over time across trials. Results: In the blinded initial phase, BAG was not significantly associated with a differential response to escitalopram over time. BAG was also not associated with a

The purpose of this discursive manuscript is to review three distinct studies that used very similar research methods, allowing the results to be critically compared. Following a series of three fictional vignettes describing various clinical scenarios managing pain, we introduce the reader to the research method of pain psychophysics.Next, we discuss how the three research studies described compliment and contrast one another. The discursive review format offers nurses an overview of a research method seldom used by nursing scientists. Psychophysical experiments allow a unique opportunity to examine the neurobiology and psychology of the pain experience in people with dementia.

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