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Compassionate self-care for nurses and midwives: A sequential explanatory mixed methods study
No full textBackground
Awareness of being kind and compassionate to yourself is often overlooked despite emerging evidence that high levels of self-compassion decrease levels of anxiety and improves well-being.
Aim
To explore what being compassionate to yourself means to nurses and midwives and increase knowledge and understanding of how self-compassion may enhance well-being.
Methods
Mixed methods study design was utilised to investigate and explore the influence of self-compassion on nurses and midwives' well-being. Phase 1 involved quantitative data collection and analysis. Phase 2 qualitative data were collected to expand on quantitative results.
Results
Fifty-four participants responded to a pre-workshop questionnaire, 55 completed immediate post-questionnaire, 28 completed a follow-up questionnaire six-eight weeks later. Five participants were interviewed to gain more insights of their experiences. Quantitative results showed significant increase in self-compassion scores immediately post-workshop, with an estimated improvement of 0.26 points (95 % CI: 0.14, 0.38, P < 0.001). Participants' anxiety significantly decreased by 0.5 units (95 % CI: −0.64, −0.36, P < 0.001). Mood changes were not statistically significant. Reflexive thematic analysis of qualitative data provided further insight into these changes resulting in three key themes: (1) “Awareness of self-compassion was the first step”, (2) “Care for yourself, before caring for others” (3) “Self-compassion strategies with everyday living activities”.
Conclusions
Self-compassion education can enhance nurses' and midwives' knowledge and understanding. Education can increase self-compassion strategies and skills to reduce anxiety levels. However, qualitative data confirmed the need for sustained reinforcement of self-compassion practices. Further research is recommended to confirm these initial findings and explore long-term impacts
Aspirin in a novel mouse model of metabolic steatotic liver disease and its potential to prevent hepatocellular carcinoma
No full textAspirin shows promise as an accessible approach for preventing liver disease and hepatocellular carcinoma (HCC). Using mouse models combining gene editing and a NASH-inducing diet, aspirin reduced liver inflammation, fibrosis, oxidative stress, and hypoxia, while shifting immune cell profiles toward tissue repair. Key targets included COX-1 and hypoxia pathways. These findings highlight aspirin’s potential as a protective agent in chronic liver disease management and warrant further investigation into its mechanisms and role in HCC prevention
Cultural and social determinants influencing mental health among adolescent mothers aged 14 -19 years during the postpartum period and their mental health care needs in Lilongwe, Malawi
No full textThis mixed methods study explored the cultural and social factors that influence adolescent mental health during the postnatal period in Malawi. The quantitative-qualitative survey was administered to adolescent mothers (n = 395). Healthcare workers and traditional practitioners participated in interviews and focus groups respectively. The study found family support, baby preparation sessions, and contact with healthcare workers positively influenced good mental health while forced marriages and limited access to mental health services were negative influences
Improving interpretation and modelling techniques in geoscience using deep generative networks (GANs)
Full text linkGenerative Adversarial Networks are a novel, data-efficient method by which models are trained to learn from a limited amount of labelled data and to generate synthetic data, which reduces the burden of manual annotation. This thesis examines the generative adversarial power of GANs in geoscience, exploring how they can be used to unravel the underlying geological patterns and geostatistical features inherent in the geoscience data
Commentary: Language Teacher Identities and Advocacy Challenging Social Inequities
No full textThis book interrogates language teacher identity construction, negotiation, and meaning-making in today's ever-changing global contexts
Understanding Policy and the Role of Planning in Enabling Urban Agriculture for Enhancing Sustainability: A Case Study of the City of Onkaparinga, South Australia
Full text linkBased on two refereed publications, this MPhil study offers a framework for integrating urban agriculture into city planning enhancing food security and fostering community engagement. By utilising vacant public land, cities can create economic opportunities, reduce environmental impacts and improve public health. Being the first in Australia, the study's methodology, empirical application to the City of Onkaparinga, South Australia and research findings provide a model for cities to replicate, promoting sustainable and resilient urban environments
Global soybean trade intensifies the impacts of dietary transition on human mobility
No full textSoybeans have experienced massive growth in trade due to diet transitions. Large imports reduce expected returns for soybean growers in importer countries, influencing occupational choices and potentially facilitating population mobility, though the distant correlation's impact remains unclear. We develop a framework integrating diet dynamics, trade indexes, and human mobility. We found: (1) dietary transition promotes human mobility through global soybean trade; (2) rural areas contribute far less than urban areas, illustrating decoupling between rural regions and international trade; (3) enhancing rural-trade coupling could improve dietary and crop flow in rural regions; (4) the study provides a new perspective on how dietary transition promotes human mobility. These findings help policymakers identify soybean trade strategies for socioeconomic development and formulate interventions to optimize population distribution. By addressing rural decoupling, we emphasize aligning rural areas with global trade to mitigate mobility pressures and socioeconomic disparities, without altering original terms
Growth, antioxidant activity and nutritional profile of Chaetomorpha linum (O.F. Müller) Kützing (1845): the effect of ionic profile and nutrient supplementation
Full text linkThis thesis investigates the growth, nutritional profile and biochemical profile of the green seaweed Chaetomorpha linum across a range of culture media types, such as media with low potassium ions, variable salinity and with nutrient supplementation. The research was conducted to investigate how the seaweed aquaculture industry in Western Australia could be diversified with a novel species which has potential applications in improving human health and cosmetic products
Group differences in biographical inventories: A meta-analysis on the adverse impact potential of biodata.
No full textThe purpose of the present meta-analysis was to determine if biodata scale scores differ based on demographic group membership (i.e., gender, race, age) and to evaluate the contextual factors that amplify or mitigate these effects (e.g., construct domain, scoring method). Despite the popularity of biodata scales for
personnel selection purposes, previous research findings do not provide clear evidence as to whether adverse impact is a serious concern. To address this gap, a meta-analysis of 43 studies (56 independent samples) was conducted, providing estimates of group differences across demographic groups (female–male, Black–White, Hispanic–White, Asian–White, and age) and relevant construct domains. The majority of biodata scale scores were found to exhibit small group differences across construct domains and demographic groups (≈75% had Cohen’s d less than |.20|). Group differences were also relatively small when compared with other popular selection methods. When considered with previous research, these findings provide further support for the use of biodata scales in personnel selection scenario
Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods
No full textEmpathy indicates an individual's ability to understand others. Over the past few years, empathy has drawn attention from various disciplines, including but not limited to Affective Computing, Cognitive Science, and Psychology. Detecting empathy has potential applications in society, healthcare and education. Despite being a broad and overlapping topic, the avenue of empathy detection leveraging Machine Learning remains underexplored from a systematic literature review perspective. We collected 849 papers from 10 well-known academic databases, systematically screened them and analysed the final 82 papers. Our analyses reveal several prominent task formulations – including empathy on localised utterances or overall expressions, unidirectional or parallel empathy, and emotional contagion – in monadic, dyadic and group interactions. Empathy detection methods are summarised based on four input modalities – text, audiovisual, audio and physiological signals – thereby presenting modality-specific network architecture design protocols. We discuss challenges, research gaps and potential applications in the Affective Computing-based empathy domain, which can facilitate new avenues of exploration. We further enlist the public availability of datasets and codes. This paper, therefore, provides a structured overview of recent advancements and remaining challenges towards developing a robust empathy detection system that could meaningfully contribute to enhancing human well-being