Hasil untuk "Mental healing"

Nicholas A. Carlisle, Peyton Miller, Gregory Pavela et al.

Purpose: Sexual and gender minority (SGM) people experience a higher prevalence of adverse health outcomes compared to their heterosexual and cisgender counterparts due, in part, to shame related to sexual orientation and gender identity. The purpose of this scoping review was to identify and synthesize empirical evidence of the effects of shame on SGM populations in the United States. Methods: Following PRISMA-ScR guidelines, we systematically searched for studies that were (1) peer-reviewed; (2) original research; (3) written in English; (4) quantitatively measuring shame; (5) among groups identifying as sexual minority, gender minority, or both; (6) within the United States. Results: We identified 22 studies meeting inclusion criteria, most of which were cross-sectional and focused on sexual minority men. Across studies, shame was consistently associated with exposure to distal and proximal stressors, general psychological processes (e.g., affective, social, and cognitive), health-compromising behaviors (e.g., substance use, sexual compulsivity), and adverse health outcomes. Notably, few studies focused on gender minority individuals, and subgroup or intersectional analyses were uncommon. Conclusion: The literature supports shame as an important correlate of health-related outcomes among SGM populations, but is limited by cross-sectional designs, heterogenous measurement approaches, and limited attention to subgroup differences. Future research should prioritize longitudinal and mechanistic studies, improve measurement harmonization, and evaluate established shame-reduction approaches with SGM-affirming adaptations and scalable delivery strategies to improve reach and advance health equity.

Meng Zheng, Yi Zhang, Feng Chen et al.

The Mental Health Gap Action Program Intervention Guide (mhGAP-IG) is an international evidence-based intervention to enhance the scalability of non-specialized workforce to address mental, neurological, and substance use disorders, including child mental and behavioral disorders (CMBDs). Implementation in China remains limited. This study sought to adapt the CMBDs-related components of the mhGAP-IG to the local context and assess the educational impacts of the adapted CMBDs-mhGAP-IG in non-specialized settings in Guangdong Province, China. Contextual adaptation was conducted through a four-step process guided by the Consolidated Framework for Implementation Research (CFIR) and the Ecological Validity Model (EVM). Qualitative data were collected via key informant interviews with six mental health experts and two focus group discussions involving seven kindergarten teachers or doctors and six general practitioners (GPs) from primary care centers. A qualitative analysis utilizing deductive and inductive thematic coding was applied. The educational impacts of a two-day training based on the adapted CMBDs-mhGAP-IG were assessed using a pre-post design among 89 participants, including kindergarten teachers or doctors and GPs. Changes in knowledge and stigma toward CMBDs were analyzed using paired t-tests. Qualitative findings identified barriers and facilitators to implementation across four CFIR domains: intervention characteristics, outer setting, inner setting, and characteristic of individuals. Barriers were concentrated in intervention characteristics (e.g., complexity, time and labor costs) and outer setting (e.g., stigma, high treatment cost). Contextual adaptation based on EVM addressed many of these barriers. Quantitative results indicated that the adapted CMBDs-mhGAP-IG training was associated with increased knowledge and reduced stigma toward CMBDs, demonstrating its educational effectiveness. This study identified barriers and facilitators to mhGAP implementation and documented a contextual adaptation process, offering insights into integrating contextual adaptation methods with implementation science. Trainings based on the adapted mhGAP-IG for non-specialists improved knowledge and stigma toward CMBDs, laying the foundation for future effectiveness evaluations.

Nadine Loh, David Luke

Accumulating psychedelic research has demonstrated a potential for improving mental health and wellbeing, yet studies in the context of eating disorders (EDs) are limited. This study aims to explore the subjective effects of psychedelic experiences to gain insight into the benefits and risks for people with EDs. Semi-structured interviews were conducted with eight adults aged 25–54 (mean age = 36.9), reporting to have had experiences with EDs and psychedelics in both naturalistic and clinical settings. Participants had multiple diagnoses and suffered chronic EDs, (mean age of onset = 13), diagnosed (<i>N</i> = 7) and undiagnosed (<i>N</i> = 1). Reports of cessation or the reduction of ED symptoms were unanimous and long-lasting for seven, with two participants reporting recovery attributed to psychedelic use. Two participants reported relapsing, attributed to environmental factors in the months following. Thematic analysis resulted in two superordinate themes, each comprising three subordinate themes. The first superordinate theme, ‘Exploring’ via the ‘gateway to healing’, illustrates mental, emotional, and transcendental elements of psychedelic experiences. The second superordinate theme, ‘Transformation’ and being ‘able to do the work’, illustrates cognitive and behavioural outcomes, with retrospective safety perceptions. These findings may provide more in-depth information on what benefits and experiences people with EDs can obtain from the use of psychedelic drugs and may inform more robust investigations of psychedelic-assisted therapy for the treatment of EDs.

Katerina Drakos, Eva Paraschou, Simay Toplu et al.

Artificial intelligence has been introduced as a way to improve access to mental health support. However, most AI mental health chatbots rely on a limited range of disciplinary input, and fail to integrate expertise across the chatbot's lifecycle. This paper examines the cost-benefit trade-off of interdisciplinary collaboration in AI mental health chatbots. We argue that involving experts from technology, healthcare, ethics, and law across key lifecycle phases is essential to ensure value-alignment and compliance with the high-risk requirements of the AI Act. We also highlight practical recommendations and existing frameworks to help balance the challenges and benefits of interdisciplinarity in mental health chatbots.

Neil Natarajan, Sruthi Viswanathan, Xavier Roberts-Gaal et al.

Static solutions don't serve a dynamic mind. Thus, we advocate a shift from static mental health diagnostic assessments to continuous, artificial intelligence (AI)-driven assessment. Focusing on Attention-Deficit/Hyperactivity Disorder (ADHD) as a case study, we explore how generative AI has the potential to address current capacity constraints in neuropsychology, potentially enabling more personalized and longitudinal care pathways. In particular, AI can efficiently conduct frequent, low-level experience sampling from patients and facilitate diagnostic reconciliation across care pathways. We envision a future where mental health care benefits from continuous, rich, and patient-centered data sampling to dynamically adapt to individual patient needs and evolving conditions, thereby improving both accessibility and efficacy of treatment. We further propose the use of mental health digital twins (MHDTs) - continuously updated computational models that capture individual symptom dynamics and trajectories - as a transformative framework for personalized mental health care. We ground this framework in empirical evidence and map out the research agenda required to refine and operationalize it.

Amaani H. Hatoum, Amy L. Burton

Abstract Eye movement desensitization and reprocessing (EMDR) has demonstrated promise as a treatment for eating disorders (ED). The present study aimed to systematically evaluate the current evidence regarding the use of EMDR therapy in the treatment of EDs, ED symptomatology and body image concerns. Included articles were original studies that described the use of EMDR therapy in the treatment of EDs, published in the English language in a peer‐review journal. The search was conducted using four electronic databases: PsycINFO, MedLine, Embase, and Web of Science. Two independent reviewers conducted screening, selection, risk of bias assessment and data extraction. Of the initial search of 109 potential studies, eight met inclusion criteria, including six case studies, one quasi‐experimental study, and one randomised control trial (RCT). The RCT indicated that including an EMDR component did not have benefits over standard treatment for core ED symptoms, whereas the quasi‐experimental study demonstrated some benefits for inclusion of EMDR as a treatment adjunct for anorexia nervosa patients. Case studies indicated some promising outcomes for patients with various presentations. Despite EMDR being an available treatment for several decades now, there is limited clinical evidence regarding its efficacy in the treatment of EDs. These findings highlight a critical need for more clinical research in this area to ensure clinical practice is guided and supported by evidence‐based outcomes.

Sunny Rai, Khushi Shelat, Devansh R Jain et al.

Culture moderates the way individuals perceive and express mental distress. Current understandings of mental health expressions on social media, however, are predominantly derived from WEIRD (Western, Educated, Industrialized, Rich, and Democratic) contexts. To address this gap, we examine mental health posts on Reddit made by individuals geolocated in India, to identify variations in social media language specific to the Indian context compared to users from Western nations. Our experiments reveal significant psychosocial variations in emotions and temporal orientation. This study demonstrates the potential of social media platforms for identifying cross-cultural differences in mental health expressions (e.g. seeking advice in India vs seeking support by Western users). Significant linguistic variations in online mental health-related language emphasize the importance of developing precision-targeted interventions that are culturally appropriate.

Cedric Donié, Marie K. Reumann, Tony Hartung et al.

Bone non-union is among the most severe complications associated with trauma surgery, occurring in 10-30% of cases after long bone fractures. Treating non-unions requires a high level of surgical expertise and often involves multiple revision surgeries, sometimes even leading to amputation. Thus, more accurate prognosis is crucial for patient well-being. Recent advances in machine learning (ML) hold promise for developing models to predict non-union healing, even when working with smaller datasets, a commonly encountered challenge in clinical domains. To demonstrate the effectiveness of ML in identifying candidates at risk of failed non-union healing, we applied three ML models (logistic regression, support vector machine, and XGBoost) to the clinical dataset TRUFFLE, which includes 797 patients with long bone non-union. The models provided prediction results with 70% sensitivity, and the specificities of 66% (XGBoost), 49% (support vector machine), and 43% (logistic regression). These findings offer valuable clinical insights because they enable early identification of patients at risk of failed non-union healing after the initial surgical revision treatment protocol.

Yusif Ibrahimov, Tarique Anwar, Tommy Yuan

Mental health constitutes a complex and pervasive global challenge, affecting millions of lives and often leading to severe consequences. In this paper, we conduct a thorough survey to explore the intersection of data science, artificial intelligence, and mental healthcare, focusing on the recent developments of mental disorder detection through online social media (OSM). A significant portion of the population actively engages in OSM platforms, creating a vast repository of personal data that holds immense potential for mental health analytics. The paper navigates through traditional diagnostic methods, state-of-the-art data- and AI-driven research studies, and the emergence of explainable AI (XAI) models for mental healthcare. We review state-of-the-art machine learning methods, particularly those based on modern deep learning, while emphasising the need for explainability in healthcare AI models. The experimental design section provides insights into prevalent practices, including available datasets and evaluation approaches. We also identify key issues and challenges in the field and propose promising future research directions. As mental health decisions demand transparency, interpretability, and ethical considerations, this paper contributes to the ongoing discourse on advancing XAI in mental healthcare through social media. The comprehensive overview presented here aims to guide researchers, practitioners, and policymakers in developing the area of mental disorder detection.

Ariel Zilberstein, Norberto Krivoy, Noa Horesh et al.

Background: A vast amount of experimental data supports the notion that Na+, K+-ATPase and endogenous cardiac steroids (ECS) such as digoxin and ouabain are involved in bipolar disorders. The aim of the study was to examine the possible beneficial effect of Digoxin-specific antibody Fab fragments Digibind, in bipolar disorder (BD) patients. Methods: In a pilot, open-label study without a control group, BD inpatient subjects received a single dose of Digibind, injected intravenously. During the 72 hrs following the intervention. the response to Digibind was measured using the Montgomery-Asberg Depression Rating Scale (MADRS). Results: A total 6 patients with a moderate to severe bipolar depressive episode (MADRS 30.3, 24–38) participated in the study. We found a significant clinical improvement at all time points after Digibind administration, manifested by a maximal decrease in the MADRS score of 8.3 points at 24 hrs, followed by a gradual rebound to baseline scores. Limitations: No control group was included, treatment was open-label, and only 6 patients were studied. Conclusions: This preliminary study showed that a one-time injection of Digibind resulted in a transient significant beneficial effect on depression symptoms in BD patients.

Viona Gunawan

Seiring dengan majunya teknologi, turut muncul kejahatan seksual yang difasilitasi teknologi, salah satunya Image-Based Sexual Abuse (IBSA) yang marak terjadi secara internasional maupun di Indonesia. Berbagai studi empiris telah dilakukan untuk meneliti faktor-faktor penyebab perilaku IBSA dan hasil yang ditemukan sangat beragam. Oleh karena itu, penulis membuat studi integrative review untuk memetakan faktor determinan perilaku IBSA. Literatur-literatur yang ditelaah oleh penulis diambil dari dua database yaitu Scopus dan Clarivate Web of Science. Dari 996 literatur yang teridentifikasi, didapatkan 13 literatur yang relevan untuk ditelaah lebih lanjut. Faktor determinan yang berhasil dipetakan dalam studi ini adalah karakteristik demografis, attitudinal, dan experiential, dark personality traits, toxic online disinhibition, konsumsi pornografi daring, serta norma gender dan interaksi homososial. Meski temuan studi-studi sebelumnya sangat beragam, tetapi terdapat beberapa temuan faktor determinan yang tidak konsisten. Maka itu, penelitian selanjutnya dapat meneliti lebih lanjut terkait inkonsistensi tersebut.

Prabin Sharma, Joanna C. Justus, Megha Thapa et al.

Mental fatigue is a leading cause of motor vehicle accidents, medical errors, loss of workplace productivity, and student disengagements in e-learning environment. Development of sensors and systems that can reliably track mental fatigue can prevent accidents, reduce errors, and help increase workplace productivity. This review provides a critical summary of theoretical models of mental fatigue, a description of key enabling sensor technologies, and a systematic review of recent studies using biosensor-based systems for tracking mental fatigue in humans. We conducted a systematic search and review of recent literature which focused on detection and tracking of mental fatigue in humans. The search yielded 57 studies (N=1082), majority of which used electroencephalography (EEG) based sensors for tracking mental fatigue. We found that EEG-based sensors can provide a moderate to good sensitivity for fatigue detection. Notably, we found no incremental benefit of using high-density EEG sensors for application in mental fatigue detection. Given the findings, we provide a critical discussion on the integration of wearable EEG and ambient sensors in the context of achieving real-world monitoring. Future work required to advance and adapt the technologies toward widespread deployment of wearable sensors and systems for fatigue monitoring in semi-autonomous and autonomous industries is examined.

Cristina Gena, Francesca Manini, Antonio Lieto et al.

This paper presents an experimental study showing that the humanoid robot NAO, in a condition already validated with regards to its capacity to trigger situational empathy in humans, is able to stimulate the attribution of mental states towards itself. Indeed, results show that participants not only experienced empathy towards NAO, when the robot was afraid of losing its memory due to a malfunction, but they also attributed higher scores to the robot emotional intelligence in the Attribution of Mental State Questionnaire, in comparison with the users in the control condition. This result suggests a possible correlation between empathy toward the robot and humans' attribution of mental states to it.

D. Jidong, T. Ike, R. Tribe et al.

ABSTRACT Beliefs and attitudes are essential in mental health discourse. However, cultural beliefs and attitudes towards mental health problems (ATMHPs) among the Berom people of Nigeria are under-researched. The present studies made original contributions using the Cultural Identity Model (CIM) as predictors to investigate ATMHPs, and semi-structured interviews to further explain the potential impact of cultural beliefs on MHPs. In study-1, N = 140 participants responded to questionnaires on ATMHPs and were analysed using multivariate multiple regression in RStudio. Study-2 interviewed N = 13 participants (n = 7 laypeople; n = 6 practitioners). Interviews were recorded, transcribed and analysed thematically. Study-1 findings showed CIM as a non-significant predictor of ATMHPs. However, in study-2, four themes emerged: Cultural beliefs that MHPs are caused by spiritual forces; Berom indigenous preference for traditional healing; Christian religious healing in Berom communities; and Western-trained mental health practitioners' perception of lay service-users. The authors concluded that the Berom traditional and Christian religious healings are beneficial.

Sanjay Kumar, Nageshwar Iyer, Mansi Jain

Fracture of mandible is a common facial injury. For restoration of normal jaw structure and function, proper bony healing is important and thus, stable plate osteosynthesis has become an indispensable component of cranio-maxillofacial surgery. Aim: This study aimed to evaluate the efficacy of 3D versus 2D mini bone plates used for internal fixation of mandibular fractures. Methods and Material: A prospective randomised control trial was conducted on 20 patients with mandibular fractures in the symphysis and parasymphysis regions. In group 1, 3-D miniplates were used with a single, rectangular, 4-holed titanium plate with 4 screws and in group 2, 2-D miniplates were used with two 4-holed titanium plates fixed with 8 screws. Time taken for fixation and operators comfort was analysed using Chi square test whereas trismus and pain were analysed using Anova. Results: • The difference for mean time taken for fixation was statistically highly significant (P < 0.01) between 3-D (33.1 minutes) and 2-D plate( 44.7 minutes). • Fixation of 2-D plate was found more comfortable. • Swelling in both the groups was generally comparable and lasted for about 1 week. •  Mouth opening in patients of both the groups showed a gradual recovery till 1 month after which it stabilized. Conclusion: 3-D miniplate were more economical, with less intraoperative time required and showed better performance over 2-D miniplates. However there was difficulty in their adaption and were inappropriate when fracture line was very close to mental foramen.

So-Min Cheong, Anne C. Black, Stephanie Wemm

Weiguang Wang, Natasha Z Foutz, Guodong Gordon Gao

Disasters, from hurricanes to pandemics, tremendously impact human lives and behaviors. Physical closeness to family post-disaster plays a critical role in mental healing and societal sustainability. Nonetheless, little is known about whether and how family colocation alters after a disaster, a topic of immense importance to a post-disaster society. We analyze 1 billion records of population-scale, granular, individual-level mobile location data to quantify family colocation, and examine the magnitude, dynamics, and socioeconomic heterogeneity of the shift in family colocation from the pre- to post-disaster period. Leveraging Hurricane Florence as a natural experiment, and Geographic Information System (GIS), machine learning, and statistical methods to investigate the shift across the landfall (treated) city of Wilmington, three partially treated cites on the hurricane's path, and two control cities off the path, we uncover dramatic (18.9%), widespread (even among the partially treated cities), and enduring (over at least 3 months) escalations in family colocation. These findings reveal the powerful psychological and behavioral impacts of the disaster upon the broader populations, and simultaneously remarkable human resilience via behavioral adaptations during disastrous times. Importantly, the disaster created a gap across socioeconomic groups non-existent beforehand, with the disadvantaged displaying weaker lifts in family colocation. This sheds important lights on policy making and policy communication to promote sustainable family colocation, healthy coping strategies against traumatic experiences, social parity, and societal recovery.

Gage M. Chu, Pauline Goger, Anne Malaktaris et al.

Background: The COVID-19 pandemic has led to psychological distress among community samples and university students. Some coping behaviors and cognitive appraisals allow individuals to experience positive psychological growth amid such a crisis (Folkman et al. 1986). In the event of continuing waves of COVID-19 infection and future viral outbreaks, understanding the relationships between coping behaviors, stress appraisals, and COVID-related distress and growth can empower public health officials and university leadership to mitigate negative consequences and encourage growth. Methods: 774 undergraduate students completed online self-report measures of coping (Brief COPE; emotion, problem, avoidant), stress appraisal (SAM; threat/centrality, challenge/self-efficacy, uncontrol, other-control), neuroticism (NEON), health anxiety (SHAI), and COVID-19 exposure/impact (C-PIQ; distress and growth). Hypotheses were examined via simple regressions and interactions. Results: Increased utilization of avoidant coping was associated with high levels of distress regardless of whether it was perceived as threatening or not. Emotion-focused and problem-focused coping strategies were associated with more growth, whereas avoidant coping was associated with less growth. Higher emotion-focused coping and challenge appraisal together predicted the most growth. Limitations: Cross-sectional design precludes the tracking of distress and growth over time; this study relied on self-report data. Conclusions: These results underscore the impact of stress appraisals on the mental health of students navigating the COVID-19 pandemic. Findings may inform public health messaging–or have clinical implications, as successful interventions exist for improving coping strategies and stress appraisals.

Nurul Hikmah, Nurul Khansa Fauziyah, Minda Septiani et al.

“Healing" has become a popular trend in Indonesia. Although it differs from the actual meaning of "healing" in a psychological perspective, "healing" describes recovery and healing in general, including the activities one does during the holidays to recover from the physical and mental boredom and fatigue one is experiencing. The literature on "healing" as a stress coping strategy in the tourism frame has not been discussed in detail in previous studies. This article aims to discuss the trend of "healing" as a stress coping strategy in the tourism context that can be practiced in tourism development in Indonesia. Coping stress is an individual response as an effort that can be made to obtain peace and inner peace against the type of stress experienced. This study uses a systematic literature review approach. There are two important findings in this study, first, coping with stress through the trend of "healing" is included in the type of emotion focused coping, which is a conscious adjustment. Second, "healing" activities which are carried out in the form of fun and calming activities such as nature tourism, culinary tourism, cultural tourism and special interest tourism. Optimizing stress coping strategies through tourism is an important step that can be taken to support the achievement of individual well-being which can develop interest in tourism visits in Indonesia

Shalini Saini, Dhiral Panjwani, Nitesh Saxena

Mental health is an extremely important subject, especially in these unprecedented times of the COVID-19 pandemic. Ubiquitous mobile phones can equip users to supplement psychiatric treatment and manage their mental health. Mobile Mental Health (MMH) apps emerge as an effective alternative to assist with a broad range of psychological disorders filling the much-needed patient-provider accessibility gap. However, it also raises significant concerns with sensitive information leakage.The absence of a transparent privacy policy and lack of user awareness may pose a significant threat to undermining the applicability of such tools. We conducted a multifold study of - 1) Privacy Policies (Manually and with Polisis, an automated framework to evaluate privacy policies); 2) App permissions; 3) Static Analysis for inherent security issues; 4) Dynamic Analysis for threat surface and vulnerabilities detection, and 5) Traffic Analysis. Our results indicate that apps' exploitable flaws, dangerous permissions, and insecure data handling pose a potential threat to the users' privacy and security. The Dynamic analysis identified 145 vulnerabilities in 20 top-rated MMH apps where attackers and malicious apps can access sensitive information. 45% of MMH apps use a unique identifier, Hardware Id, which can link a unique id to a particular user and probe users' mental health. Traffic analysis shows that sensitive mental health data can be leaked through insecure data transmission. MMH apps need better scrutiny and regulation for more widespread usage to meet the increasing need for mental health care without being intrusive to the already vulnerable population.