European Journal of Investigation in Health, Psychology and Education

18 pages, 3771 KiB

Open AccessArticle

Impact of Family Environment in Rural China on Loneliness, Depression, and Internet Addiction Among Children and Adolescents

by Yixiang Zhou, Meng Zheng, Yujie He, Jianghui Zhang, Tingting Guo, Qing Wang and Wen Chen

Eur. J. Investig. Health Psychol. Educ. 2025, 15(5), 68; https://doi.org/10.3390/ejihpe15050068 (registering DOI) - 1 May 2025

Abstract

This study investigates the impact of family environments on the psychological well-being of children and adolescents in rural China, focusing on loneliness, depression, and internet addiction. Using a cross-sectional design, data were collected from 652 families in rural Hunan province to examine the role of family communication, caregiver psychological states, and parenting styles. Structural equation modeling revealed that family economic conditions influenced children’s mental health through primary caregivers’ emotional well-being, hostile parenting styles, and family communication. Notably, hostile parenting and poor family communication were the strongest mediators across all three psychological outcomes, consistently exhibiting significant associations with loneliness, depression, and internet addiction. The findings highlight the crucial role of parenting styles and poor family communication in shaping children’s psychological health, emphasizing the need for interventions that enhance family communication and promote supportive parenting. Addressing caregiver emotional well-being and adjusting parental expectations may serve as effective strategies for improving mental health outcomes among rural youth. Full article

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27 pages, 405 KiB

Open AccessArticle

Time Perspective, Psychological Well-Being and Attitudes to Seeking Mental Health Services in Russian Y and Z Generations

by Evgeniy B. Bashkin, Galina N. Kameneva, Vsevolod Konstantinov, Irina A. Novikova, Tatiana S. Pilishvili, Marina A. Rushina and Dmitriy A. Shlyakhta

Eur. J. Investig. Health Psychol. Educ. 2025, 15(5), 67; https://doi.org/10.3390/ejihpe15050067 (registering DOI) - 29 Apr 2025

Viewed by 135

Abstract

Help-seeking of mental health professionals remains low, even in countries where mental health care is highly accessible. Therefore, it is necessary to continue research on the sociodemographic and psychological factors of attitudes toward seeking mental health services (ATSMHS) in different countries. The purpose of the present study was to identify the associations of the ATSMHS with psychological well-being and time perspective in the Russian Y and Z generations. The Y-Generation sample included 217 (69.9% female) respondents aged 22 to 39 years, and the Z-Generation sample included 256 (82.8% female) respondents aged 17 to 21 years. Russian versions of the Psychological Well-Being Scale (PWBS) by Ryff, Zimbardo Time Perspective Inventory (ZTPI), and Inventory of Attitudes toward Seeking Mental Health Services (IASMHS) by Mackenzie and colleagues were used for diagnostics. The main research findings show that (1) Personal growth is a positive predictor and the Fatalistic present is a negative predictor of ATSMHS in both generations, and (2) additional positive predictors of ATSMHS in the Y–Generation are Positive relations and Hedonistic present, and in the Z–Generation it is Future. These findings are important for developing programs to maintain, preserve, and prevent mental health across generations. Full article

(This article belongs to the Special Issue Subjective Time: Cognition, Emotion and Beyond)

18 pages, 365 KiB

Open AccessArticle

Health and Psychological Predictors of Antibiotic Use in Infancy and Fathers’ Role

by Marina Fuertes, Rita Almeida and Francisco Dionisio

Eur. J. Investig. Health Psychol. Educ. 2025, 15(5), 66; https://doi.org/10.3390/ejihpe15050066 - 25 Apr 2025

Viewed by 227

Abstract

Previous research has shown that antibiotic use during the first year is associated with infants’ difficult behavior, maternal low sensitivity, and infant insecure-ambivalent attachment. However, these results may depend on the extent and type of parental involvement, paternal stress related to infant care, or the infant’s exposure to infections. To explore this question, we analyzed the relationship between these factors and examined potential predictors of antibiotic use among demographic, health, and psychological variables. This study included 62 Portuguese infants and their fathers as participants. Demographic and health information was collected at birth, 3, 9, and 12 months. Father–infant interactive behavior was observed in free play at 3 months and infant attachment in Strange Situation at 12 months. Parental Stress and Parents’ Responsibility Scales were used at 9 and 12 months, respectively. Infants who received antibiotics in the first year were less cooperative, more difficult, and less passive in free-play interactions and were more likely to attend a center-based daycare than others. In this study, the predictors of antibiotic use are infant difficultness and daycare type. Full article

(This article belongs to the Special Issue Parenting across the Lifespan: Perinatal Mental Health, Infant Feeding, and Child Development)

19 pages, 2579 KiB

Open AccessArticle

Predicting Workplace Hazard, Stress and Burnout Among Public Health Inspectors: An AI-Driven Analysis in the Context of Climate Change

by Ioannis Adamopoulos, Antonios Valamontes, Panagiotis Tsirkas and George Dounias

Eur. J. Investig. Health Psychol. Educ. 2025, 15(5), 65; https://doi.org/10.3390/ejihpe15050065 - 22 Apr 2025

Viewed by 331

Abstract

The increasing severity of climate-related workplace hazards challenges occupational health and safety, particularly for Public Health and Safety Inspectors. Exposure to extreme temperatures, air pollution, and high-risk environments heightens immediate physical threats and long-term burnout. This study employs Artificial Intelligence (AI)-driven predictive analytics and secondary data analysis to assess hazards and forecast burnout risks. Machine learning models, including eXtreme Gradient Boosting (XGBoost 3.0), Random Forest, Autoencoders, and Long Short-Term Memory (LSTMs), achieved 85–90% accuracy in hazard prediction, reducing workplace incidents by 35% over six months. Burnout risk analysis identified key predictors: physical hazard exposure (β = 0.76, p < 0.01), extended work hours (>10 h/day, +40% risk), and inadequate training (β = 0.68, p < 0.05). Adaptive workload scheduling and fatigue monitoring reduced burnout prevalence by 28%. Real-time environmental data improved hazard detection, while Natural Language Processing (NLP)-based text mining identified stress-related indicators in worker reports. The results demonstrate AI’s effectiveness in workplace safety, predicting, classifying, and mitigating risks. Reinforcement learning-based adaptive monitoring optimizes workforce well-being. Expanding predictive-driven occupational health frameworks to broader industries could enhance safety protocols, ensuring proactive risk mitigation. Future applications include integrating biometric wearables and real-time physiological monitoring to improve predictive accuracy and strengthen occupational resilience. Full article

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Eur. J. Investig. Health Psychol. Educ., Volume 15, Issue 5 (May 2025) – 4 articles

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