Search Results (4,975)

Background: A significant body of research has identified ongoing unmet needs among cancer survivors. However, there is limited information about the survivorship experiences of patients in low- and middle-income countries. This study examined the experiences of colorectal cancer (CRC) survivors in Thailand to understand their post-treatment needs and priorities. Methods: A qualitative descriptive study using semi-structured interviews was conducted with 24 colorectal cancer survivors purposively recruited from a large university hospital in Bangkok, Thailand. The interviews lasted 60–90 min, were audio-recorded, and transcribed verbatim. The data were analyzed using hybrid content analysis, guided by the five domains of the Survivors Unmet Needs Survey (SUNS): information, emotional health, financial concerns, access and continuity of care, and relationships. Results: Participants had a mean age of 57 (SD = 10.9) years. Most were male (58.3%) and diagnosed at a late stage (62.5%). All participants had undergone surgical treatment, and the average time since treatment completion was approximately 3.85 (SD = 2.8) years. Participants most often reported unmet needs in the information and emotional health domains. They also described inadequate physical infrastructure (e.g., overcrowded clinic spaces) and minimal involvement of multidisciplinary providers. Despite these challenges, survivors expressed strong trust in physicians and a preference for physician-led care. Telehealth was viewed as a potential solution to reduce access barriers and improve continuity of care. Conclusions: Guided by the SUNS framework, this study identified significant unmet needs among colorectal cancer survivors in Thailand, particularly in health information, psychological support, and care system infrastructure. Strengthening health information delivery, establishing dedicated survivorship clinics, and exploring scalable care models supported by telehealth could help bridge these gaps and promote more equitable survivorship care in low-resource settings. Full article

This study investigates how transformational leadership impacts pro-environmental and proactive work behaviors through key employee psychological states: self-efficacy, change orientation, and positive affect. We argue that transformational leadership significantly enhances these psychological states, which can drive proactive and pro-environmental workplace behaviors. We used survey data collected via Amazon Mechanical Turk (MTurk) from 542 full-time employees in the United States. Data analysis used Partial Least Squares Structural Equation Modeling (PLS-SEM). The results confirm that transformational leadership significantly enhances self-efficacy, change orientation, and positive affect, factors that in turn promote pro-environmental work behavior. Moreover, change orientation and positive affect (but not self-efficacy) favor proactive work behavior. These findings emphasize the role of employee psychological mechanisms in translating leadership into sustainable workplace behavior. The study contributes to the leadership and sustainability literature by clarifying how internal psychological resources act as behavioral catalysts. Leaders can formulate strategies focusing on emotional and cognitive empowerment. Limitations and future research directions are also discussed. Full article

Trust in public institutions was challenged during the COVID-19 global pandemic, with widespread mistrust towards healthcare institutions as well as fellow public institutions. Concurrently, a new public institution or social tool, mass-market artificial intelligence (AI), more broadly emerged, which too may be a target of fluctuating public trust. Using national survey data from the United Kingdom’s Centre for Data Ethics and Innovation (survey year: 2022, N = 4320; survey year: 2023, N = 4232), we explore the level of trust in civic institutions (healthcare, non-healthcare, and AI) during and immediately after the COVID-19 pandemic in the United Kingdom using a naturalistic quasi-experimental design. At both waves (2022 and 2023), principal component analysis and structural equation modeling over thirteen public institutions and AI variables confirmed three factors (or domains) of public trust: trust in healthcare institutions, trust in fellow civic institutions other than healthcare, and trust in AI. Measurement invariance testing of mean levels of public trust along each distinct component revealed that as compared with 2022, in 2023, (1) trust in healthcare institutions and in fellow civic institutions other than healthcare significantly increased and (2) trust in AI remained approximately level. Next, latent profile modeling revealed four levels of a common public trust profile, with all three domains of public trust being normatively closely associated. Taken together, these results suggest that a psychological stance of public trust, PT, may increase after a societal crisis. Full article

Background: Mindfulness and mind wandering are cognitive traits central to attentional control and psychological well-being, yet their neural underpinnings are yet to be elucidated. This study aimed to identify structural brain networks comprising gray matter (GM) and white matter (WM) that predict individual differences in mindfulness and distinct mind wandering tendencies (deliberate and spontaneous). Methods: Using structural MRI data and self-report measures from 76 participants, we applied an unsupervised data-fusion machine learning technique (parallel independent component analysis) to identify GM and WM networks associated with mindfulness and mind wandering traits. Results: Our analysis revealed several distinct brain networks linked to these cognitive constructs. Specifically, one GM network involving subcortical regions, including the caudate and thalamus, positively predicted mindfulness and deliberate mind wandering, while negatively influencing spontaneous mind wandering through the mediating role of the mindfulness facet “acting with awareness.” In addition, two separate WM networks, predominantly involving frontoparietal and temporal regions, were directly associated with reduced spontaneous mind wandering. Conclusions: These findings advance our current knowledge by demonstrating that specific GM and WM structures are involved in mindfulness and different forms of mind wandering. Our results also show that the “acting with awareness” facet has a mediating effect on spontaneous mind wandering, which provides supporting evidence for attentional and executive control models. These new insights into the neuroanatomical correlates of mindfulness and mind wandering have implications for ongoing research in the growing topic of mindfulness and mind wandering, mindfulness-based interventions, and other clinical applications. Full article

Background/Objectives: Post-discharge interventions addressing psychological, informational, and practical needs of brain tumor surgery patients are limited. This study aimed to develop and examine the effects of a post-discharge care program for patients with benign brain tumors who underwent surgery. Methods: A quasi-experimental study with a nonequivalent control group pretest–post-test non-synchronized design was employed. The post-discharge care program was developed using the ADDIE model and delivered as an 8-week, 8-session program to 65 discharged patients (Intervention: n = 33, Control: n = 32). Outcomes were measured using the Memorial Symptom Assessment Scale (MSAS) for symptom clusters, Post-Discharge Coping Difficulty Scale (PDCDS) for post-discharge adaptation, and Functional Assessment of Chronic Illness Therapy (FACIT) for quality of life. Results: Significant group × time interactions were found between intervention and control groups for symptom clusters (F = 74.878, p < 0.001), post-discharge adaptation (F = 144.687, p < 0.001), and all quality of life domains: physical (F = 38.996, p < 0.001), social/family (F = 50.865, p < 0.001), emotional (F = 39.110, p < 0.001), and functional (F = 38.917, p < 0.001). The intervention group showed clinically meaningful improvements across all outcomes. Conclusions: This study demonstrates that the post-discharge care program was effective in improving symptom clusters, post-discharge adaptation, and quality of life in patients with benign brain tumors who underwent surgery. The program can contribute to achieving better health outcomes for this population in clinical practice. Full article

Compared to non-smart communities, smart communities expand the boundaries of community management and provide a platform for the deep integration of fitness and health. However, a single-entity governance model reduces the management efficiency of smart communities and hinders the realization of fitness-health integration within them. A collaborative governance model involving governments, businesses, social organizations, and residents replaces the traditional linear governance model that relies on a single entity through resource integration. This study, based on collaborative governance theory, employs three scenario-based experimental designs and quantitative analysis, with Xiamen’s smart city community and non-smart urban village community as research subjects. It explores the multistakeholder collaborative governance model for the deep integration of fitness and health, compares the differences in fitness-health integration between smart communities and non-smart communities, and measures the effectiveness differences between multistakeholder collaborative governance and single-entity governance models. The findings indicate: (1) Residents in smart communities have higher satisfaction with comprehensive fitness-health services; (2) Residents in smart communities perceive shorter psychological distances when engaging in fitness-health activities compared to non-smart environments; (3) The governance model moderates the impact of psychological distance on service satisfaction. Compared to the single-actor model, multiactor collaborative governance more effectively enhances perceived psychological proximity and improves satisfaction. The research findings contribute theoretically to advancing understanding of collaborative governance theory while expanding the application of the technology acceptance model (TAM) and the construal level theory in the context of community governance. Practically, they offer insights for public policymakers to optimize resource allocation and for community managers to strengthen digital governance. Full article

Background/Objectives: Anorexia–Cachexia Syndrome (ACS) is a multifactorial condition common in advanced chronic illnesses, leading to significant impacts on prognosis and quality of life. This retrospective cohort study aimed to evaluate the prevalence, management strategies, and clinical and patient-centered outcomes of ACS in a home-based palliative care team. Methods: Clinical records of 128 adult patients followed between 2021 and 2024 were analyzed. Data collected included sociodemographic variables, clinical diagnosis, nutritional parameters (Palliative Performance Scale (PPS), Mini Nutritional Assessment (MNA)), symptoms (anorexia, fatigue), interventions (enteral nutrition, psychological and rehabilitative support), and relevant medications. Statistical analysis included descriptive, inferential, and multivariable proportional hazard regression analysis to identify independent predictors of weight loss and anorexia. Results: Manifestations of ACS were observed across both oncologic and non-oncologic conditions. The prevalence of weight loss and anorexia were interrelated and were not different between diagnostic groups. Using multivariable analysis, higher baseline MNA scores (HR = 3.797, p = 0.006) and the use of enteral nutrition (HR = 7.418, p = 0.014) were independently associated with an increased risk of significant weight loss. Lower baseline PPS scores (HR = 0.069), use of enteral nutrition (HR = −0.890), and the presence of psychological support were protective for subsequent anorexia. Dexamethasone use was associated with greater nutritional decline in univariate models. Conclusions: The management of ACS in home palliative care requires the early identification of symptoms, multidisciplinary intervention, and personalized strategies beyond disease etiology. Risk of weight loss is associated with higher MNA scores, and these are best managed in the first week. In anorexia cases, psychological support is protective. Full article

Background and Objectives: Data on mental health in university students have been increasingly concerning, with high prevalence rates of clinical conditions such as anxiety, stress, and depression. This study aims to evaluate the risk factors associated with mental health status and to develop a predictive model. Materials and Methods: A total of 242 university students were recruited (74.8% women). Participants’ ages ranged from 18 to 56 years (M = 25.81; SD = 7.59). Data collection were conducted through the Depression, Anxiety, and Stress Scale (DASS-21), the Big Five Inventory-10 (BFI-10), and the Coping Orientation to Problems Experienced Inventory (COPE-28). Results: Overall, mean scores across the three clinical dimensions are within the moderate range, but anxiety shows the highest mean value (M = 8.67, SD = 5.69) and is categorized as “extremely severe.” Additionally, identifying as female, living with family or roommates, and having high scores on passive coping styles were significant risk factors for mental health deterioration. In contrast, identifying as male, living with a romantic partner (cohabitation), and having high scores on the Responsibility personality trait were identified as protective factors against mental health impairment. Conclusions: Additional research is warranted to explore additional mediating variables and to develop specific intervention protocols for improving university students’ psychological well-being. Full article

Mental disorders (MDs) constitute significant risk factors for self-harm and suicide. The incidence of MDs has been increasing annually, primarily due to inadequate diagnosis and intervention. Early identification and timely intervention can effectively slow the progression of MDs and enhance the quality of life. However, the high cost and complexity of in-hospital screening exacerbate the psychological burden on patients. Moreover, existing studies primarily focus on the identification of individual subcategories and lack attention to model explainability. These approaches fail to adequately address the complexity of clinical demands. Early screening of MDs using EEG signals and deep learning techniques has demonstrated simplicity and effectiveness. To this end, we constructed a Dual-Branch Network (DBN) leveraging resting-state Quantitative Electroencephalogram (QEEG) features. The DBN is designed to enable the detection of multiple categories of MDs. Firstly, a dual-branch feature extraction strategy was designed to capture multi-dimensional latent features. Further, we propose a Multi-Head Attention Mechanism (MHAM) that integrates dynamic routing. This architecture assigns greater weights to key elements and enhances information transmission efficiency. Finally, the diagnosis is derived from a fully connected layer. In addition, we incorporate SHAP analysis to facilitate feature attribution. This technique elucidates the contribution of significant features to MD detection and improves the transparency of model predictions. Experimental results demonstrate the effectiveness of DBN in detecting various MD categories. The performance of DBN surpasses that of traditional machine learning models. Ablation studies further validate the architectural soundness of DBN. The DBN effectively reduces screening complexity and demonstrates significant potential for clinical applications. Full article

Background/Objectives: Psychosocial factors are not routinely screened for during the perioperative period, even though they significantly influence overall health. This study aimed to inventory the psychosocial vulnerabilities among patients undergoing cancer surgery. Methods: We conducted a cross-sectional analysis of a researcher-administered psychosocial screener implemented within a statewide health system between July 2023 and August 2025. A 45 min screener was offered to consecutive adult patients within two weeks before their major elective cancer surgery. Residential addresses were geocoded to assign neighborhood deprivation percentiles for the Area Deprivation Index (ADI) and the Social Vulnerability Index (SVI). Elevated psychosocial vulnerability was determined based on a model-based clustering approach, and a high deprivation index was defined as ≥75th percentile. Results: A total of 383 patients (37% response rate) completed the screener, including colorectal (40%), thoracic (36%), and surgical oncology (24%) patients, with a median age of 66 years (IQR, 57–73). Over half (52.0%, n = 199) reported ≥2 psychological and ≥2 social vulnerabilities. Younger patients (p = 0.021), non-white patients (p < 0.001), patients identifying as non-heterosexual (p = 0.014), without a partner (p < 0.001) or private insurance (p = 0.040), and those with lower household income (p < 0.001) were more likely to report elevated psychosocial vulnerability. Patients with elevated psychosocial vulnerability were more likely to reside in deprived neighborhoods (ADI: 34.0 vs. 29.0, p = 0.035; SVI: 0.35 vs. 0.27, p = 0.005). Conclusions: Patients undergoing major cancer surgery experience substantial psychosocial vulnerabilities, particularly those from socioeconomically disadvantaged neighborhoods. Future work should identify the psychosocial factors most predictive of poor surgical outcomes to guide targeted preoperative interventions. Full article

Background: This study explores post-traumatic growth (PTG) among parents of childhood cancer survivors (CCSs), a group often underrepresented in research. Method: A convergent parallel mixed-methods design integrating Bayesian Multilevel Latent Class Analysis and Thematic Analysis was utilized in a longitudinal study involving 58 caregivers (50 mothers, 8 fathers) from the Children’s Clinical University Hospital in Riga. Quantitative data were collected at diagnosis using the Psychosocial Assessment Tool (PAT) and Big Five Inventory-10 (BFI-10). Follow-up assessments post-treatment included the Responses to Stress Questionnaire (RSQ), Impact of Event Scale-Revised (IES-R), and the Post-traumatic Growth Inventory (PTGI). Qualitative data were collected through structured interviews. Results: A 2-class model distinguished parents with low PTG from those with moderate to high PTG. Change in values, detachment from trivial stressors, and acceptance of life emerged as key indicators of growth. PTG was not significantly correlated with overall post-traumatic stress symptoms, but engagement coping strategies showed a positive association with PTG and personality traits like extraversion and openness. Conclusions: The mixed methods approach revealed sample-specific PTG elements not reflected in standardized tools. Initial perceptions of the cancer diagnosis shaped psychological outcomes, with PTG facilitated by adaptive coping, self-reflection, support, emotional disclosure, and psychological struggle. This study offers the first insights into PTG among Latvian parents of CCSs, a previously unexplored area. Full article

The rapid integration of generative AI, particularly ChatGPT, into academic settings has prompted urgent questions regarding its impact on students’ psychological and academic outcomes. Although generative AI holds considerable potential to transform educational practices, its effects on individual traits such as self-control and academic wellbeing remain insufficiently explored. This study addresses this gap through a sequential two-phase design. In the first phase, the ChatGPT Usage Scale was adapted and validated for a Turkish university student population (N = 413). Using confirmatory factor analysis and item response theory, the scale was confirmed as a psychometrically valid and reliable one-factor instrument. In the second phase, a separate sample (N = 449) was used to examine the relationships between ChatGPT usage, self-control, and academic wellbeing through a mediation model. The findings revealed that higher ChatGPT usage was significantly associated with lower levels of both self-control and academic wellbeing. Additionally, mediation analysis demonstrated that self-control partially mediates the negative relationship between ChatGPT usage and academic wellbeing. The study concludes that while generative AI tools are valuable, their integration into education presents a double-edged sword, highlighting the critical need to foster students’ self-regulatory skills to ensure they can harness these tools responsibly without compromising their academic and psychological health. Full article

This study investigates how perceptual and emotional factors—perceived naturalness, aesthetic pleasure, environmental concern, and disgust—shape consumer acceptance of a human-hair-derived bio-fabricated textile product (a unisex cardholder). In a scenario-based online survey, participants viewed an AI-generated image accompanied by a short vignette. A purposive sample of young adults in Istanbul with prior experience purchasing sustainable textile products was recruited and screened. All constructs were measured with standard Likert-type scales and translated into Turkish using a two-way back-translation procedure. Data were analyzed with PLS-SEM. Model fit was acceptable, and the model accounted for a substantial share of the variance in adoption intention. Aesthetic pleasure showed a clear positive influence on adoption intention, whereas perceived naturalness did not display a direct effect. Environmental concern modestly strengthened the link between naturalness and adoption. Disgust emerged as the dominant moderator, fully conditioning the naturalness pathway and reducing—but not eliminating—the effect of aesthetic pleasure. Together, these findings indicate that perceived naturalness, aesthetic pleasure, environmental concern, and disgust jointly shape adoption intention and that practical emphasis should be placed on reducing feelings of disgust while enhancing aesthetic appeal. Full article

Background: Problematic Internet gaming (PIG), considered an early stage of Internet gaming addiction (IGA), has become increasingly prevalent among adolescents. This study focused on deviant peer affiliation (DPA) and hedonic gaming experience (HGE) as key mediators and examined four psychosocial risk factors closely related to them: interpersonal incompetence (II), perceived stress (PS), frustration (FR), and emotional loneliness (EL). Specifically, the study investigated how these four psychosocial risk factors influence adolescents’ DPA, HGE, and PIG, and whether DPA and HGE mediate these relationships. Methods: Based on existing validated scales, we developed a questionnaire to measure these seven constructs (II, PS, FR, IC, DPA, HGE, and PIG), proposed 14 hypotheses, and collected 214 valid responses from adolescents. Structural equation modeling (SEM) was used to test the hypothesized model. Findings: The results showed that all 14 hypotheses were supported. Specifically, interpersonal incompetence significantly predicted perceived stress; stress led to frustration; and frustration, in turn, contributed to emotional loneliness. Furthermore, all four psychosocial risk factors significantly predicted deviant peer affiliation, hedonic gaming experience, and ultimately, problematic Internet gaming among adolescents. Both DPA and HGE mediated the effects of psychosocial risk factors on adolescent problematic Internet gaming (PIG), with the model explaining moderate-to-high variance. This study highlights the importance of segmenting adolescents into more specific subgroups based on the distinct developmental pathways leading to PIG. Implications: Understanding the step-by-step mechanisms and psychological drivers of different adolescent subtypes can provide a more solid foundation for early identification and targeted intervention efforts. Full article

Research articles are valuable resources for Information Retrieval and Natural Language Processing (NLP) tasks, offering opportunities to analyze key components of scholarly content. This study investigates the presence of methodological terminology in psychology research over the past 30 years (1995–2024) by applying a novel NLP and Machine Learning pipeline to a large corpus of 85,452 abstracts, as well as the extent to which this terminology forms distinct thematic groupings. Combining glossary-based extraction, contextualized language model embeddings, and dual-mode clustering, this study offers a scalable framework for the exploration of methodological transparency in scientific text via deep semantic structures. A curated glossary of 365 method-related keywords served as a gold-standard reference for term identification, using direct and fuzzy string matching. Retrieved terms were encoded with SciBERT, averaging embeddings across contextual occurrences to produce unified vectors. These vectors were clustered using unsupervised and weighted unsupervised approaches, yielding six and ten clusters, respectively. Cluster composition was analyzed using weighted statistical measures to assess term importance within and across groups. A total of 78.16% of the examined abstracts contained glossary terms, with an average of 1.8 term per abstract, highlighting an increasing presence of methodological terminology in psychology and reflecting a shift toward greater transparency in research reporting. This work goes beyond the use of static vectors by incorporating contextual understanding in the examination of methodological terminology, while offering a scalable and generalizable approach to semantic analysis in scientific texts, with implications for meta-research, domain-specific lexicon development, and automated scientific knowledge discovery. Full article