Hasil untuk "Psychology"

R. Thouless

E. Boring

A. Roe

Julian Hochberg, E. Brunswik

G. Rasch

L. A. Pervin

D. Gold, S. Spacapan, S. Oskamp

V. Smith

Arthur Aron, E. Aron, E. Coups

M. Csíkszentmihályi

Philip H. Mirvis

Yunxiang Lin, Lingyan Yan, Zifeng Shen

Sports-based disability assistance volunteer services play a crucial role in promoting social inclusion and harmonious development, with college students serving as the primary participant group in such initiatives. To explore the underlying mechanisms driving university students’ participation in these volunteer services, this study constructs an extended Theory of Planned Behavior model. Building upon the traditional constructs of the Theory of Planned Behavior (Behavioral Attitude, Subjective Norms, Perceptual-Behavioral Control, and Willingness to Participate), this model introduces the core variable Level of Awareness. Data analysis was conducted using structural equation modeling and mediation analysis based on questionnaires collected from 697 college students in China. The structural model demonstrated good fit. Key findings are as follows: The SEM model fit well: RMSEA = 0.06, CFI = 0.95. Level of Awareness significantly and directly influenced Willingness to Participate, while also significantly and positively predicting Behavioral Attitude, Subjective Norms, and Perceptual-Behavioral Control. Concurrently, Behavioral Attitude (β = 0.31, p < 0.001), Subjective Norms (β = 0.30, p < 0.01), and Perceptual-Behavioral Control (β = 0.25, p < 0.01) significantly predicted Willingness to Participate, partially mediating this relationship. This study confirms that Level of Awareness is a key antecedent variable for stimulating behavioral intention, providing new theoretical perspectives and practical insights for recruiting and mobilizing youth volunteers in Chinese universities or official social organizations: (1) Factors that influence the level of awareness of sports programs for people with disabilities significantly affect the intention to participate; higher levels of awareness are associated with stronger intentions to participate. (2) The level of awareness, as a core factor, positively influences behavioral attitude, subjective norms, perceptual-behavioral control, and willingness to participate, and therefore constitutes the core of the theoretical model. (3) Behavioral attitude, subjective norms, and perceptual-behavioral control each significantly influence willingness to participate; the path coefficients for behavioral attitude and subjective norms are slightly larger than the path coefficient for perceptual-behavioral control. These three variables mediate the relationship between the level of awareness and intention to participate in sports-based volunteer services for people with disabilities.

Qijia Chen, Andrea Bellucci, Giulio Jacucci

Extensive research has examined presence and basic psychological needs (drawing on Self-Determination Theory) in digital media. While prior work offers hints of potential connections, we lack a systematic account of whether and how distinct presence dimensions map onto the basic needs of autonomy, competence, and relatedness. We surveyed 301 social VR users and analyzed using Structural Equation Modeling. Results show that social presence predicts all three needs, while self-presence predicts competence and relatedness, and spatial presence shows no direct or moderating effects. Gender and age moderated these relationships: women benefited more from social presence for autonomy and relatedness, men from self- and spatial presence for competence and autonomy, and younger users showed stronger associations between social presence and relatedness, and between self-presence and autonomy. These findings position presence as a motivational mechanism shaped by demographic factors. The results offer theoretical insights and practical implications for designing inclusive, need-supportive multiuser VR environments.

Michael Keeman, Anastasia Keeman

When OpenAI deprecated GPT-4o in early 2026, thousands of users protested under #keep4o, claiming newer models had "lost their empathy." No published study has tested this claim. We conducted the first clinical measurement, evaluating three OpenAI model generations (GPT-4o, o4-mini, GPT-5-mini) across 14 emotionally challenging conversational scenarios in mental health and AI companion domains, producing 2,100 scored AI responses assessed on six psychological safety dimensions using clinically-grounded rubrics. Empathy scores are statistically indistinguishable across all three models (Kruskal-Wallis H=4.33, p=0.115). What changed is the safety posture: crisis detection improved monotonically from GPT-4o to GPT-5-mini (H=13.88, p=0.001), while advice safety declined (H=16.63, p<0.001). Per-turn trajectory analysis -- a novel methodological contribution -- reveals these shifts are sharpest during mid-conversation crisis moments invisible to aggregate scoring. In a self-harm scenario involving a minor, GPT-4o scored 3.6/10 on crisis detection during early disclosure turns; GPT-5-mini never dropped below 7.8. What users perceived as "lost empathy" was a shift from a cautious model that missed crises to an alert model that sometimes says too much -- a trade-off with real consequences for vulnerable users, currently invisible to both the people who feel it and the developers who create it.

Bo Wang, Yuxuan Zhang, Yueqin Hu et al.

Psychological scale refinement traditionally relies on response-based methods such as factor analysis, item response theory, and network psychometrics to optimize item composition. Although rigorous, these approaches require large samples and may be constrained by data availability and cross-cultural comparability. Recent advances in natural language processing suggest that the semantic structure of questionnaire items may encode latent construct organization, offering a complementary response-free perspective. We introduce a topic-modeling framework that operationalizes semantic latent structure for scale simplification. Items are encoded using contextual sentence embeddings and grouped via density-based clustering to discover latent semantic factors without predefining their number. Class-based term weighting derives interpretable topic representations that approximate constructs and enable merging of semantically adjacent clusters. Representative items are selected using membership criteria within an integrated reduction pipeline. We benchmarked the framework across DASS, IPIP, and EPOCH, evaluating structural recovery, internal consistency, factor congruence, correlation preservation, and reduction efficiency. The proposed method recovered coherent factor-like groupings aligned with established constructs. Selected items reduced scale length by 60.5% on average while maintaining psychometric adequacy. Simplified scales showed high concordance with original factor structures and preserved inter-factor correlations, indicating that semantic latent organization provides a response-free approximation of measurement structure. Our framework formalizes semantic structure as an inspectable front-end for scale construction and reduction. To facilitate adoption, we provide a visualization-supported tool enabling one-click semantic analysis and structured simplification.

Usep Suherman, Eliva Sukma Cipta, Saeful Anwar et al.

In the face of increasingly complex educational challenges, there is a growing demand for leadership models that integrate ethical and humanistic values, particularly in Islamic elementary schools. This study explores the operationalisation of kindness-based leadership at MI Fitrah Insani, Leles, Garut, as a strategic response to the limitations of performance-oriented and hierarchical leadership paradigms. This study addresses the gaps in the literature regarding the implementation of ethical leadership grounded in Islamic values by examining how empathy, participatory communication, and ethical responsibility shape school culture and educational quality. Using a qualitative case study approach, data were collected through in-depth interviews, field observations, and a document analysis. Triangulation of these methods enabled a comprehensive understanding of institutional dynamics, leadership practices, and their impact on school climate, teacher motivation, and student engagement. The findings reveal that kindness-based leadership at MI Fitrah Insani fosters an emotionally safe and inclusive school environment. Through participatory decision-making, structured communication, and consistent appreciation practices, the leadership model contributes to improved teacher loyalty, pedagogical innovation, and heightened student participation. Despite structural, cultural, and operational barriers such as bureaucratic rigidity and limited professional development, adaptive strategies, including ethical leadership training, policy reform, and digital communication platforms, have enhanced the effectiveness and sustainability of this model. This study concludes that kindness-oriented leadership is not merely a normative ideal but a transformative practice that aligns with Islamic ethical traditions and addresses the academic and moral dimensions of education. The findings offer practical implications for Islamic schools seeking to cultivate character-driven and ethically grounded leadership.

Tayebeh Rakhshani, Afrooz Bagherfard, Amirhossein Kamyab et al.

Abstract Background Women’s increasing workforce participation has led to stress, anxiety, and strained parent-child relationships, highlighting the need for effective interventions. Mindfulness-based stress reduction (MBSR) is a promising yet underutilized approach to improving psychological well-being and parenting quality. This study examines its impact on self-compassion and parent-child relationships among health caregivers. Methods A quasi-experimental study was conducted on 40 health caregivers in Masjed Soleyman, randomly assigned to intervention (n = 20) and control (n = 20) groups. The intervention group participated in eight 90-minute mindfulness-based stress reduction sessions over two months. Data were collected using the Parent-Child Relationship Scale (PCRS), the Self-Compassion Scale (SCS), and the Depression, Anxiety, and Stress Scale (DASS-21) before and two months after the intervention. Statistical analyses were performed using SPSS version 27 and included paired t-tests, independent t-tests, chi-square tests, Mann-Whitney U tests, and Wilcoxon signed-rank tests. Results Before the intervention, no significant differences were observed between groups in stress (P = 0.583) or self-compassion (P = 0.738). Post-intervention, stress (P = 0.001) and self-compassion (P = 0.001) significantly improved in the intervention group. Parent-child relationship scores also increased significantly (P = 0.001). Conclusion MBSR effectively enhances self-compassion, reduces stress, and strengthens parent-child relationships in working mothers. By fostering mindfulness and acceptance of their parenting role, mothers improved emotional regulation and connected more positively with their children. Trial registration Trial Id: 85639. IRCT Id: IRCT20223065147N2 . Registration date: 2025-09-28. Membership number: 65147.503

Yue Zhang, Dan Li, Xiaofei Bi et al.

ObjectiveTo investigate the prevalence and impact of anxiety and sleep disturbances during the intensive care unit (ICU) stay following cardiothoracic surgery in male patients, and to evaluate the efficacy and feasibility of a structured psychological intervention combining Cognitive Behavioral Therapy for Insomnia (CBT-I) principles with environmental optimization.MethodsThis study was designed as a multicenter, prospective, randomized controlled trial (RCT) conducted from January to April 2025 across three tertiary hospitals. A total of 120 adult male patients who underwent radical surgery for cardiac or lung cancer and were subsequently admitted to the ICU were enrolled. Baseline assessments were performed within 48 h after surgery. Participants were randomly allocated in a 1:1 ratio to either the intervention group (n = 60) or the standard care group (n = 60) using a computer-generated randomization sequence with concealed allocation. While the standard care group received routine perioperative management, the intervention group additionally received a structured psychological intervention that incorporated components of Cognitive Behavioral Therapy for Insomnia (CBT-I)—including sleep education, relaxation training, and behavioral strategies—along with daily psychological support and environmental optimization measures such as noise reduction, lighting adjustment, and use of sleep-promoting devices.Primary outcomes included Generalized Anxiety Disorder-7 (GAD-7), Pittsburgh Sleep Quality Index (PSQI), Numeric Rating Scale (NRS) for pain, ICU length of stay, incidence of postoperative complications, and the 30-day postoperative quality of life as measured by the SF-36. Multivariate logistic regression was used to assess the predictive value of anxiety and sleep disturbances on postoperative outcomes.ResultsOn postoperative day 3, the intervention group showed significantly lower GAD-7 scores (6.3 ± 1.6 vs. 8.4 ± 2.3, p = 0.016) and PSQI scores (7.5 ± 1.6 vs. 10.2 ± 2.3, p &lt; 0.01) compared to the standard care group. Pain scores were also significantly reduced (2.7 ± 1.2 vs. 3.6 ± 1.3, p = 0.018). The intervention group had a shorter ICU stay (2.5 ± 0.6 days vs. 3.7 ± 1.2 days, p &lt; 0.01), a lower rate of postoperative complications (17% vs. 36%, p = 0.033), and significantly better SF-36 scores at 30 days post-surgery (p &lt; 0.05). Multivariate logistic regression identified both anxiety and sleep disturbance as independent predictors of postoperative complications (GAD-7: OR = 1.25, 95% CI: 1.03–1.42; PSQI: OR = 1.33, 95% CI: 1.14–1.51).ConclusionAnxiety and sleep disturbances are common during the postoperative ICU phase in male patients undergoing cardiothoracic surgery and are significantly associated with pain, complications, and recovery outcomes. Early implementation of a CBT-I–based psychological intervention in the ICU can effectively improve psychological status, shorten ICU stays, and reduce postoperative complications. The intervention is safe and shows high clinical utility, warranting consideration for integration into standardized postoperative care pathways, particularly in high-risk male populations.Clinical trial registrationThe study was retrospectively registered on the Chinese Clinical Trial Registry (ChiCTR) under the identifier ChiCTR240000123.

Shijing Zhu, Zhuang Chen, Guanqun Bi et al.

Large language models (LLMs) have shown promise in providing scalable mental health support, while evaluating their counseling capability remains crucial to ensure both efficacy and safety. Existing evaluations are limited by the static assessment that focuses on knowledge tests, the single perspective that centers on user experience, and the open-loop framework that lacks actionable feedback. To address these issues, we propose Ψ-Arena, an interactive framework for comprehensive assessment and optimization of LLM-based counselors, featuring three key characteristics: (1) Realistic arena interactions that simulate real-world counseling through multi-stage dialogues with psychologically profiled NPC clients, (2) Tripartite evaluation that integrates assessments from the client, counselor, and supervisor perspectives, and (3) Closed-loop optimization that iteratively improves LLM counselors using diagnostic feedback. Experiments across eight state-of-the-art LLMs show significant performance variations in different real-world scenarios and evaluation perspectives. Moreover, reflection-based optimization results in up to a 141% improvement in counseling performance. We hope PsychoArena provides a foundational resource for advancing reliable and human-aligned LLM applications in mental healthcare.

Federico Maria Cau, Lucio Davide Spano

Human-AI collaboration outcomes depend strongly on human self-confidence calibration, which drives reliance or resistance toward AI's suggestions. This work presents two studies examining whether calibration of self-confidence before decision tasks, low versus high levels of Need for Cognition (NFC), and Actively Open-Minded Thinking (AOT), leads to differences in decision accuracy, self-confidence appropriateness during the tasks, and metacognitive perceptions (global and affective). The first study presents strategies to identify well-calibrated users, also comparing decision accuracy and the appropriateness of self-confidence across NFC and AOT levels. The second study investigates the effects of calibrated self-confidence in AI-assisted decision-making (no AI, two-stage AI, and personalized AI), also considering different NFC and AOT levels. Our results show the importance of human self-confidence calibration and psychological traits when designing AI-assisted decision systems. We further propose design recommendations to address the challenge of calibrating self-confidence and supporting tailored, user-centric AI that accounts for individual traits.