European Journal of Investigation in Health Psychology and Education最新文献

Large language models (LLMs) offer promising possibilities in mental health, yet their ability to assess disorders and recommend treatments remains underexplored. This quantitative cross-sectional study evaluated four LLMs (Gemini (Gemini 2.0 Flash Experimental), Claude (Claude 3.5 Sonnet), ChatGPT-3.5, and ChatGPT-4) using text vignettes representing conditions such as depression, suicidal ideation, early and chronic schizophrenia, social phobia, and PTSD. Each model's diagnostic accuracy, treatment recommendations, and predicted outcomes were compared with norms established by mental health professionals. Findings indicated that for certain conditions, including depression and PTSD, models like ChatGPT-4 achieved higher diagnostic accuracy compared to human professionals. However, in more complex cases, such as early schizophrenia, LLM performance varied, with ChatGPT-4 achieving only 55% accuracy, while other LLMs and professionals performed better. LLMs tended to suggest a broader range of proactive treatments, whereas professionals recommended more targeted psychiatric consultations and specific medications. In terms of outcome predictions, professionals were generally more optimistic regarding full recovery, especially with treatment, while LLMs predicted lower full recovery rates and higher partial recovery rates, particularly in untreated cases. While LLMs recommend a broader treatment range, their conservative recovery predictions, particularly for complex conditions, highlight the need for professional oversight. LLMs provide valuable support in diagnostics and treatment planning but cannot replace professional discretion.

This study aimed to examine the associations between parents' smartphone use during conversations with children, children's emotional and behavioral reactions to parents' smartphone use, and children's well-being. This study was conducted on a sample of 284 children (aged 10 to 15 years, with a mean age of 12.23 in 2021; 40.2% boys). The data come from a four-wave longitudinal study (2021-2023) within the project D.E.C.I.D.E. Children reported how often their parents use smartphones during conversations with them (second wave), their emotions and behaviors related to parents' smartphone use (third wave), and their subjective well-being (fourth wave). A proposed model was tested in which the frequency of parents' smartphone use during parent-child conversations was a predictor, different children's emotional and behavioral reactions to parents' smartphone use were mediators, and children's well-being was the criterion. The results showed that more frequent parents' smartphone use is associated with more frequent children's experiences of anger and sadness in situations when parents use smartphones while with children, which is linked to lower children's well-being. More frequent parents' smartphone use is associated with more giving up on seeking parents' attention among children, which is related to lower well-being.

Health disparities represent one of the most pressing challenges in modern healthcare systems worldwide (Shadmi et al [...].

Artificial intelligence (AI) has transformed healthcare, yet patients' acceptance of AI-driven medical services remains constrained. Despite its significant potential, patients exhibit reluctance towards this technology. A notable lack of comprehensive research exists that examines the variables driving patients' resistance to AI. This study explores the variables influencing patients' resistance to adopt AI technology in healthcare by applying an extended Ram and Sheth Model. More specifically, this research examines the roles of the need for personal contact (NPC), perceived technological dependence (PTD), and general skepticism toward AI (GSAI) in shaping patient resistance to AI integration. For this reason, a sequential mixed-method approach was employed, beginning with semi-structured interviews to identify adaptable factors in healthcare. It then followed with a survey to validate the qualitative findings through Structural Equation Modeling (SEM) via AMOS (version 24). The findings confirm that NPC, PTD, and GSAI significantly contribute to patient resistance to AI in healthcare. Precisely, patients who prefer personal interaction, feel dependent on AI, or are skeptical of AI's promises are more likely to resist its adoption. The findings highlight the psychological factors driving patient reluctance toward AI in healthcare, offering valuable insights for healthcare administrators. Strategies to balance AI's efficiency with human interaction, mitigate technological dependence, and foster trust are recommended for successful implementation of AI. This research adds to the theoretical understanding of Innovation Resistance Theory, providing both conceptual insights and practical implications for the effective incorporation of AI in healthcare.

Developing effective cognitive training tools for older adults, specifically addressing executive functions such as planning, is a challenging task. It is of paramount importance to ensure the implementation of engaging activities that must be tailored to the specific needs and expectations of older adults. Furthermore, it is essential to provide the appropriate level of complexity for the planning task. A human-centred approach was used to address the issues identified in the design of the tool. Two pilot studies were conducted with older adults to fine-tune the training task and optimize its suitability for them. This also led to an enhancement of the underlying planning engine, transitioning from a simple fast-forward planner (PDDL4J) to an advanced heuristic search planner (ENHSP). The results show that user studies enabled the development of a cognitive training system that gradually increased the proposed difficulty levels of the planning task while maintaining usability and satisfaction among older adults. This highlights the importance of conducting user studies when implementing cognitive training tools for older adults.

Rural teachers have the potential to be important agents of local development. To achieve this goal, they need to acquire high digital competence in order to effectively integrate technology into their pedagogical practices, thus enriching the learning experience of students and fostering their participation. Digital competence contributes to reducing the education gap between urban and rural areas, promoting educational equity and inclusion. High digital competence also enables rural teachers to address the specific challenges of their environment, such as cultural diversity, scarce resources, and low population density. Against this backdrop, this article presents a bibliometric review of the importance of digital competence in rural teachers in Spain, where the problem of rural depopulation, as in other regions of Europe, has been accentuated in recent years. The objective of the bibliometric review is both (i) to find the strengths and weaknesses that concern researchers in relation to the digital training of teachers in rural areas and (ii) to express them explicitly in order to contribute to propose solutions. The results reveal the growing academic and political attention being paid to this issue, highlighting the need for rural teachers to acquire digital skills to adapt to current educational demands. In addition, they point to the importance of developing specific policies and programs in Europe as well as providing training opportunities and ongoing support to ensure that teachers in rural contexts can acquire or strengthen their digital competence, thereby improving the quality of education in these areas.

As stated by the Goal Orientation Theory, students want to achieve a goal for multiple reasons, with each having a different impact on academic performance. This framework encompasses a three-factor model of goal achievement: a mastery goal, a performance-avoidance (PAv) goal, and a performance-approach (PAp) goal. Students may experience elevated stress levels and burnout due to adopting an ineffective approach to goal achievement. This can lead to a loss of interest in studies and even physical and psychological exhaustion. In severe cases, this may result in students abandoning their studies early. This study aims to integrate these factors into a comprehensive model. A cross-sectional study comprising 1497 Italian university students examined the mediating role of academic burnout (professional efficacy, cynicism, and emotional exhaustion) in the association between achievement goals (mastery, PAv, and PAp goals) and the intention to drop out (ID). The questionnaires were administered from October 2022 to September 2023. Structural equation modeling was employed to evaluate the association between variables. The results of the mediation analysis indicate that cynicism and professional efficacy fully mediate the association between mastery and dropout. Cynicism (β = -0.28, p < 0.001) and professional efficacy (β = -0.17, p < 0.001) were both negatively associated with ID, while they partially mediate the association between PAv goals and ID (cynicism: β = 0.21, p ≤ 0.001; professional efficacy: β = 0.05, p ≤ 0.001), and between PAp goals and ID via professional efficacy (β = -0.04, p ≤ 0.001). This study contributes to the currently limited literature on the relationship between achievement goals, burnout, and ID in a sample of university students. The findings of this study may have useful implications for the application of interventions that impact students' well-being and academic success, potentially limiting their possible dropout.

Background: Fibromyalgia (FM) is a complex condition marked by increased pain sensitivity and central sensitization. Studies often explore the link between FM and depressive anxiety disorders, but few focus on dysthymia or persistent depressive disorder (PDD), which can be more disabling than major depression (MD). Objective: To identify clinical scales and subscales of the Personality Assessment Inventory (PAI) that effectively describe and differentiate the psychological profile of PDD, with or without comorbid MD, in FM patients with PDD previously dimensionally classified by the Millon Clinical Multiaxial Inventory III (MCMI-III). Method: An observational, cross-sectional study was conducted with 66 women (mean age 49.18, SD = 8.09) from Hospital del Mar. The PAI, the MCMI-III, and the Fibromyalgia Impact Questionnaire (FIQ) were used to assess the sample. Results: The PAI showed strong discriminative ability in detecting PDD, characterized by high scores in cognitive and emotional depression and low scores in identity alteration, dominance, and grandeur. High scores in cognitive, emotional, and physiological depression, identity alteration, cognitive anxiety, and suicidal ideation, along with low scores in dominance and grandeur, were needed to detect MD with PDD. Discriminant analysis could differentiate 69.6-73.9% of the PDD group and 84.6% of the PDD+MD group. Group comparisons showed that 72.2% of patients with an affective disorder by PAI were correctly classified in the MCMI-III affective disorder group, and 70% without affective disorder were correctly classified. Conclusions: The PAI effectively identifies PDD in FM patients and detects concurrent MD episodes, aiding in better prognostic and therapeutic guidance.

Studies have shown that the school atmosphere perceived by students can play a key role in promoting their well-being and success in school. No study to date has analyzed whether the students' perceived school atmosphere might contribute to school refusal (SR), which in turn might reduce students' engagement and promote underachievement. A cross-sectional study was conducted with 528 Italian high school students (M_age = 16.08, SD = 1.38; 50.8% males, 47% females, and 2.3% not declared), with the aim of assessing the role of the mediation of SR (Anxious Anticipation, Difficult Transition, Interpersonal Discomfort, and School Avoidance) in the association between students' perceived school atmosphere (Student Relations, Student-Teacher Relations, Educational Climate, Sense of Belonging, and Interpersonal Justice) and school engagement and underachievement. Data were collected using validated instruments, including the SChool REfusal EvaluatioN for school refusal, the Multidimensional School Climate Questionnaire for school atmosphere, and the Utrecht Work Engagement Scale for school engagement. To evaluate the association between variables, we performed structural equation modeling with latent variables. Mediation analysis indicated that Difficult Transition fully mediates the association between Sense of Belonging and school engagement (β = 0.20, p ≤ 0.05). This study extends the knowledge of school refusal behavior.

Developmental coordination disorder (DCD/Dyspraxia) is a commonly misunderstood and under-recognized specific learning difficulty (SpLD) in educational settings. This lifelong condition affects fine and gross motor coordination and significantly interferes with many activities of daily living, academic achievement, and employment opportunities. However, most Higher Education Institutions (HEIs) are unaware of its prevalence within their context, even though 5% of the general population have DCD and the enrolment of students in UK Higher Education with a known disability has increased by 42.4% between 2018 and 2023. Thus, understanding the lived experiences of students with DCD within Higher Education in the UK remains a considerable gap in knowledge. Through the use of focus groups, the lived experiences of 10 students with DCD at two UK HEIs were investigated. The thematic analysis identified four main themes: 'Awareness of DCD', 'Participation in Higher Education for individuals with DCD', 'Wellbeing', and 'Everyday living'. Students shared that HEIs appeared to lack awareness of DCD and felt they had an inability to specify the correct support at university. Importantly, whilst the students in the study were not always confident in identifying the specific support they needed, they shared the strategies they used to aid their university experience. The students described the physical toll that many everyday living tasks can take, which subsequently negatively impact academic participation and wellbeing. On a positive note, many of the students discussed positive experiences at university, such as enjoying their own autonomy (and flexibility) to be independent and inform strategies for their own learning. Importantly, the findings from this work highlight the complexity and heterogeneity of DCD and the need for a tailored approach to supporting individuals with this condition. Given the importance of educational qualifications to enter the workplace, and the contribution of employment to quality of life, these findings help signpost areas where HEIs can improve the experiences of students with DCD that may also enhance academic success.