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This study longitudinally investigated the predictors and mediators of adolescent smartphone addiction by examining the impact of parental smartphone addiction at T1 on adolescent smartphone addiction at T3, as well as the separate and sequential role of adolescent self-esteem and depression at T2 as mediating factors.

This study used a hierarchical regression and the PROCESS macro (Model 6) to investigate research model by collecting 3,904 parent-adolescent pairs. Panel data were collected from three waves of the Korean Children and Youth Panel Survey (KCYPS).

First, the result showed that parental smartphone addiction at T1 significantly and positively predicted adolescent smartphone addiction at T3. Second, the serial mediation analysis revealed that the impact of parental smartphone addiction at T1 on adolescent smartphone addiction at T3 was mediated by adolescent self-esteem and depression at T2 independently and serially.

The findings enhance our comprehension of the impact of parental smartphone addiction, adolescent self-esteem and depression, on adolescent smartphone addiction.

The study aims to identify the predictors of depression and anxiety among carers of hospitalized patients with mental illness in Eradah Complex for Mental Health Hospital.

A descriptive correlational study design was conducted using a convenient sample of family carers of patients with mental illness (N = 216). The study used the Beck Depression Inventory and Anxiety Inventory scales.

The results of regression models revealed that the socio-demographic characteristics of the family carers showed that age is a statistically significant predictor of family carers depression and anxiety scores. In addition, the age of the family carers explained 36% of the variance in the family carers depression and anxiety scores, while marital status explained 64% of the total variance in the family carers’ depression and anxiety scores. Furthermore, having received support in caring significantly predicted depression and anxiety scores, and this was the case for occupation status and being diagnosed with any form of chronic illness.

Based on the findings of this study, the authors opine that evaluations of carers’ cognitive strategies and social support are needed to determine the risk of depression in carers of mental patients.

This study is the one of the limited studies conducted in Saudi Arabia to identify predictor of depression and anxiety among caregivers of hospitalized patients with mental illness. The study has used a validated scales to assess the main study outcomes.

Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.

The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.

(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.

The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.

This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.

The purpose of this paper is to analyze the relationship of the organization and whether employees in the companies operating in the textile sector receive leadership support when they experience depression.

Data were obtained from personnel working in textile companies in organized industrial zones located within five provinces: Istanbul, Ankara, Bursa, Izmir and Antalya (defined as industrial cities of Turkey). The reason for choosing these companies was related to the question of whether the mental state of the personnel may have had an impact on the success of the company, especially as the textile industry works with more manpower and knowledge due to the nature of the job. The moderation relationship of leadership support to the relationship of this situation on organizational culture and organizational commitment was analyzed using the SmartPLS program.

As a result of the analyzes, it was determined that the depression of the employees weakens the organizational commitment, thereby leading to a negative relationship within the organizational culture. But, with leadership support, the organizational commitment increases and the organizational culture is positively affected.

As the research was conducted in companies in the textile sector in Istanbul, this limitation should be taken into account in future research. In addition, as data is collected from white-collar employees in the administrative staff position (the sample group), this situation should also be taken into account. Considering the questions asked in the questionnaire, it is recommended that future research be conducted on blue-collar workers.

It can be concluded that the leadership role is an important factor for organizations to prevent employees from being depressed and employees should receive positive support to ensure organizational commitment. At the same time, it can be concluded that the organizational culture is positively affected if the depression of the employees has decreased.

The research is an original study in terms of investigating the relationship of depression status to the leadership support of employees working in textile companies in an environment where competition is consistently intense.

This paper aims to explore the prevalence of depression, anxiety symptoms and suicidal ideation among the Romani population in Ukraine and their connections with various social health determinants: age, gender, household characteristics, employment and living conditions.

For measuring mental health conditions, GAD-7 and PHQ-9 were used. Individual interviews were conducted by trained volunteers of the International Charitable Organization “Roma Women’s Foundation Chirikli”. Data were gathered from January to March 2020.

The overall level of depression found in the sample was 8.08, while the mean for anxiety was 7.22. In general, 32.7% of respondents scored positively for signs of depression and 29.6% for anxiety. The two-week prevalence of suicidal ideations was 26.9%. Compared to the general population, the prevalence of depression among the Romani research participants was twofold higher, and anxiety was 2.5-fold higher. Signs of depression and anxiety in women were significantly higher (36% vs 28.6% for depression and 33.9% vs 24.2% for anxiety) than in men. Signs of depression and anxiety were higher for people without education than for university students (9.32 vs 3.04 for depression and 8.26 vs 3.00 for anxiety). The lowest levels of depression, anxiety and suicidal ideation were among officially married persons (6.61, 6.36 and 0.23, respectively). Significant small positive correlations were found between all measurements and the number of household members (0.149 for depression, 0.124 for suicidal ideation and 0.175 for anxiety; p < 0.001) and the number of children (0.303 for depression, 0.224 for suicidal ideation and 0.243 for anxiety; p < 0.001). In terms of employment, the highest scores for depression, anxiety and suicidal ideation were found among those who are employed seasonally (9.06, 8.25 and 0.61) or irregularly (9.09, 8.12 and 0.57) in contrast with self-employed (4.88, 4.90 and 0.19) and full-time employees (5.86, 5.51 and 0.18). Living place (city, village or camp) showed no relation with mental health, except for suicidal ideation: those living in villages had higher levels of suicidal ideation than those living in cities (0.49 vs 0.31).

The study has some limitations. Data were gathered from January to March 2020, and since then, the situation in Ukraine has drastically changed due to the full-scale Russian invasion. While this study’s data and conclusions might serve as a baseline for further research, they do not represent the real-time situation. While many social factors were analysed, the effects found for them do not necessarily represent causality, given the statistical methods used. Interactions among factors were not studied; therefore, no firm conclusions can be made about the effects of those interactions on mental health.

To the best of the authors’ knowledge, this paper is original in terms of its topic, as the first-ever in Ukraine quantitative study of mental health and social determinants of mental health of the Romani population.

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.

This study aims to explore the evolutionary pattern of discussion topics over time in an online depression self-help community.

Using the Latent Dirichlet Allocation (LDA) method, the authors analyzed 17,534 posts and 138,567 comments posted over 8 years on an online depression self-help group in China and identified the major discussion topics. Based on significant changes in the frequency of posts over time, the authors identified five stages of development. Through a comparative analysis of discussion topics in the five stages, the authors identified the changes in the extent and range of topics over time. The authors discuss the influence of socio-cultural factors on depressed individuals' health information behavior.

The results illustrate an evolutionary pattern of topics in users' discussion in the online depression self-help group, including five distinct stages with a sequence of topic changes. The discussion topics of the group included self-reflection, daily record, peer diagnosis, companionship support and instrumental support. While some prominent topics were discussed frequently in each stage, some topics were short-lived.

While most prior research has ignored topic changes over time, the study takes an evolutionary perspective of online discussion topics among depressed individuals. The authors provide a nuanced account of the progression of topics through five distinct stages, showing that the community experienced a sequence of changes as it developed. Identifying this evolutionary pattern extends the scope of research on depression therapy in China and offers a deeper understanding of the support that individuals with depression seek, receive and provide online.

Using the lenses of social exchange and reactance theories, this study examines the relationships between abusive supervision and both turnover intentions and job satisfaction. The moderating role of employee depression in the relationship between abusive supervision and these specific work outcomes is also investigated, by incorporating the conservation of resources theory.

Quantitative data were collected from a sample of 221 frontline retail employees, across shopping malls in the Caribbean nation of Trinidad. A 3-step multiple hierarchical regression analysis was performed to test the relationships.

The findings provided support for the propositions that abusive supervision predicts job satisfaction and turnover intentions, respectively. Employee depression moderated the relationship between abusive supervision and job satisfaction but did not moderate the relationship between abusive supervision and turnover intentions.

While existing research has explored the relationships between abusive supervision, job satisfaction and turnover intentions, limited studies have investigated the moderating role of employee depression. This study contributes to understanding this pervasive workplace issue by investigating a relatively unexplored moderating effect.

Perceptions of ageing towards the self and towards others can positively and negatively impact an older adult’s mental wellbeing. This paper aims to consolidate literature examining the relationship between perceptions of ageing and depression in older adults to inform both practice and policy for older adult mental health services.

Quantitative research articles examining perceptions of ageing and depression in older adults were identified through searches on three electronical databases, alongside forward and backwards citation searches. A total of 14 articles involving 31,211 participants were identified.

Greater negative attitudes towards ageing were associated with higher levels of depressive symptoms and greater positive attitudes towards ageing were associated with lower levels of depressive symptoms or higher levels of happiness. However, the causal direction of this relationship could not be determined. Studies demonstrated that perceptions of ageing also act as a moderator in the relationship between depression and health status, hopelessness and personality traits. Future research should attempt to examine the relationship between perceptions of ageing and depression in older adults to attempt to identify the causal direction of this relationship.

This is the only systematic review the authors are aware of consolidating literature which explores the relationship between older adults’ perceptions of ageing and depression. It is hoped that these findings will be able to inform both policy and practice to improve older adults’ care and support for depression.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.