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This paper makes a conceptual contribution by intersecting two strands of literature (return to work following health issues and industrial relations) to facilitate our understanding of the potential role of social dialogue in supporting return to work (RTW) following the diagnosis of a chronic illness. It conceptualises the levels and channels through which various actors and their interactions may play a role in RTW facilitation within the actor-centred institutional framework.

The paper uses an exploratory design based mainly on desk research but is also informed by roundtable discussions done in six countries as part of a larger project.

The conceptual and analytical framework (CAF) is developed to explain how various actors interact together in ways shaped by the RTW policy framework and the industrial relations systems, resulting in a continuum of RTW facilitation situations.

There is limited research on return-to-work policies following diagnosis of chronic illness from a comprehensive actor-oriented perspective. The existing literature usually focusses on just one stakeholder, overlooking the role of social dialogue actors. By bridging the two streams of literature and incorporating all potential actors and their interactions in a unitary model, the proposed framework provides a valuable tool to further discuss how successful RTW after a diagnosis of chronic illness can be facilitated.

This study examined dimensions of young-adulthood development in lived experience reported by young persons (19 women, 18 men) with the disabling condition of kidney failure requiring chronic dialysis or kidney transplantation. In semistructured phone interviews, participants (ages 23–37) described their family/living situation, employment and community activity, current situation, and experience. Participants' qualitative responses about “the way you see things, do things, feel about things” and “how you feel about yourself” were examined to identify themes. Limited achievement of proposed “successful” dimensions of young adulthood characterized the study cohort, based on indicators included in the interview. In qualitative data, the theme of perceived stigma and spoiled identity (Goffman, 1963) was reflected in comments offered by participants regarding their self-confidence and motivation to pursue goals. A second theme in participants' qualitative responses was a sense of isolation from age peers who shared their condition, and participants expressed frustration around having an age-inappropriate condition (“why me?”). Perceived stigma and spoiled identity impact social ties and life goals and are understudied influences in the life course trajectory of young persons with kidney failure and the challenges inherent in navigating health status and developmental life course transitions.

This study aims to systematically review the health risk assessment of the concentration of heavy metals in Pistacia species globally.

The authors systematically searched PubMed, Science Direct, Scopus and Google Scholar to identify all articles published between 1 January 2002 and 20 August 2022. A total of 33 studies met the authors’ inclusion criteria, and their data were extracted. Additionally, the potential risk to human health was assessed by calculating the target hazard quotient and hazard index for both child and adult consumers.

The estimated daily intake for heavy metals in the included studies ranged from 9.72 × 10^–9 to 7.35 (mg/day) in the following order: zinc (Zn) > mercury (Hg) > iron (Fe) > lead (Pb) > copper (Cu) > aluminum (Al) > nickel (Ni) > chromium (Cr) > manganese (Mn) > cadmium (Cd) > arsenic (As) > selenium (Se) > cobalt (Co). Among the studies that investigated heavy metals in Pistacia species around the world, the non-carcinogenic risk for all species of Pistacia was determined to be less than 1, except for Pb and Hg in Pistacia lentiscus.

The soil near the industrial area contained excessive amounts of heavy metals, which led to the transfer of heavy metals to plants. Owing to the insufficiency of the number of studies that examined heavy metals in Pistacia species, further monitoring and investigations were recommended.

Domestic violence is a universal social concern, and its implication on children’s mental health is a global health predicament. Although the aftereffect of domestic violence on the mental health of women is well researched, mental health disorders of children in terms of attention deficit hyperactivity disorder (ADHD), conduct disorder (CD), oppositional defiant disorder (ODD), general anxiety disorder (GAD), separation anxiety disorder (AD) and mood disorder (MD) on account of domestic violence is rarely investigated. Hence, the purpose of the study is to investigate the impact of exposure to domestic violence on children’s mental health.

To examine the correlation between domestic violence and mental health among children, in a survey of public primary schools in Ekiti State, Nigeria, the authors employed 664 pupils with simple random techniques. The Multidimensional Domestic Violence Scale assessed the participants’ exposure to domestic violence. The Child & Youth Mental Health General Screening Questionnaire was used to examine respondents’ mental health.

The results revealed a high incidence of domestic violence among children. It also showed a substantial correlation between domestic violence and the mental health of primary school pupils. Children living with domestic violence experience significantly increased rates of ADHD, ODD, AD, GAD and MD as forms of mental health disorders.

Given the unpleasantness of this social issue affecting the well-being of children globally, it is imperative to engage in intervention and prevention packages that will assist the victims, perpetrators, parents, teachers, schools and other stakeholders. Introducing counselling services into the primary school curriculum will assist victims and those at risk develop self-confidence that will enable them to report their violators. Although counselors do not diagnose, early detection of victims would assist in early intervention where counselors can use different techniques and approaches to help such victims to resolve all forms of cognitive distortions that can lead to mental health disorders. Counselling services can help in providing referral opportunities to victims or “at risk children,” rehabilitation and follow-up services for victims of domestic violence.

Government organizations should also reintroduce health-care services where clinical psychologists will be available in primary schools to provide psychological services to meet the needs of the victims.

Conclusively, the study presents evidence that children who witness domestic violence face a significant risk of developing mental health disorders. The findings highlight the potential harm that domestic violence can inflict on children’s mental well-being. Mental health conditions such as ADHD, CD, ODD, GAD, AD and MD are commonly observed in these children. It recommends establishing early intervention programs for children witnessing domestic violence in schools and communities and programs to detect and address mental health issues promptly.

Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

A direct correlation exists between waste disposal, disease spread and public health. This article systematically reviewed healthcare waste and its implication for public health. This review identified and described the associations and impact of waste disposal on public health.

This paper systematically reviewed the literature on waste disposal and its implications for public health by searching Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA), PubMed, Web of Science, Scopus and ScienceDirect databases. Of a total of 1,583 studies, 59 articles were selected and reviewed.

The review revealed the spread of infectious diseases and environmental degradation as the most typical implications of improper waste disposal to public health. The impact of waste includes infectious diseases such as cholera, Hepatitis B, respiratory problems, food and metal poisoning, skin infections, and bacteremia, and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife.

Infectious diseases such as cholera, hepatitis B, respiratory problems, food and metal poisoning, skin infections, bacteremia and environmental degradation such as land, water, and air pollution, flooding, drainage obstruction, climate change, and harm to marine and wildlife are some of the public impacts of improper waste disposal.

Healthcare industry waste is a significant waste that can harm the environment and public health if not properly collected, stored, treated, managed and disposed of. There is a need for knowledge and skills applicable to proper healthcare waste disposal and management. Policies must be developed to implement appropriate waste management to prevent public health threats.

This study aims to examine the volume of ehealth literacy documents during 2006–2022, and the nature of citation of ehealth documents by country, organizations, sources and authors.

The study adopted a bibliometric approach. Bibliographic data was collected on citation of ehealth documents by country, organizations, sources and authors from Scopus and mapped and visualized the citations using VosViewer.

A total of 1,176 documents were produced during 2006–2022, indicating a high rate of document production in this sub-discipline. Among the 102 countries that contributed documents on the subject, 58 qualified for the analysis. The USA had the highest number of cited documents on eHealth literacy, followed by Canada and Australia. The average publication year for the USA was 2018, with 348 publications and an average of 24.12 citations. Canada had a high average citation count of 44.69. Furthermore, the document examined citations by organizations.

The research implications of the study suggest that eHealth literacy is an actively growing field of research, with a substantial impact on the academic community, and researchers should focus on collaboration with high-impact institutions and journals to increase the visibility and recognition of their work, while also paying attention to the need for more research representation from African countries.

The study’s findings indicate a high rate of document production and growing interest in eHealth literacy research, with the USA leading in the number of cited documents followed by Canada, while Canadian eHealth literacy research receives relatively higher citation rates on average than the USA.

The study’s originality lies in its examination of citation patterns and global contributions to eHealth literacy literature, offering valuable insights for researchers. It identifies key authors, high-impact journals and institutions, providing valuable guidance for collaboration. The research highlights a growing interest in eHealth literacy, underscoring its potential impact on public health and digital health interventions.

Background: Quality of life is a core concern for cancer patients, which can be negatively affected by illness-related death anxiety; yet understanding of how to appropriately target psycho-oncological interventions remains lacking. We aimed to explore experiential acceptance in cancer patients, and whether acceptance – as an alternative to avoidant coping – was related to and predictive of better quality of life and death anxiety outcomes.

Methods: We used a longitudinal, quantitative design with a follow-up after three months. Seventy-two participants completed a questionnaire-battery measuring illness appraisals, acceptance and non-acceptance coping-styles, quality of life, and death anxiety; 31 participants repeated the battery after three months.

Results: Acceptance was an independent explanatory and predictive variable for quality of life and death anxiety, in the direction of psychological health. Acceptance had greater explanatory power for outcomes than either cancer appraisals or avoidant response styles. Avoidant response styles were associated with greater death anxiety and poorer quality of life.

Conclusions: The findings support the role of an accepting response-style in favourable psychological outcomes, identifying a possible target for future psychological intervention. Response styles that might be encouraged in other therapies, such as active coping, planning, and positive reframing, were not associated with beneficial outcomes.

This study aims to provide an overview and quality appraisal of the current scientific evidence concerning the prevalence and characteristics of mental and physical disorders among sentenced female prisoners.

A mixed-methods systematic literature review.

A total of 4 reviews and 39 single studies met the inclusion criteria for the review. Mental disorders were the main area of investigation in the majority of single studies, with substance abuse, particularly drug abuse, as the most consistently gender biased disorder, with higher prevalence among women than men in prison. The review identified a lack of updated systematic evidence on the presence of multi-morbidity.

This study provides an up-to-date overview and quality appraisal of the current scientific evidence concerning the prevalence and characteristics of mental and physical disorders among female prisoners.

The Internet is used as a tool to seek health information by individuals. Mental health concerns are the high prevalence of the novel coronavirus disease 2019 (COVID-19) and preventive steps are required to curb the illness. Therefore, to gain more insight into health concerns, it is now a common practice to seek health information on the Internet. This study propose an integrated theoretical model to explore the relationship between COVID-19 protocols and perceived online trust with online health information seeking intention (OHISI) and a moderating effect of perceived severity and perceived urgency.

Data are collected from 325 athletes in the category of individual and team sports through an online survey in a Likert-scale questionnaire. The analysis is performed with a quantitative methodology.

The study reveals the bright side of online health information (OHI), which brings athletes together and has played out with virtual happy hours, meetings and events. The bright side of OHI reflects social, cultural, technological and economic benefits. An OHI chatbot offers bright personalised side information to the individual seeker, which is more convenient and efficient than human capabilities.

The pivotal contribution is the integrated theoretical framework that is derived from multidisciplinary literature to capture the complexity of OHI. Also, it conceptualises the constructs in the context of OHI and COVID-19.