Search

Depression or mood disorder is a common illness that impacts mentally, depression affects the way people act and think. Depression is identified by a loss of pleasure and interest in activities they usually enjoy. In the present century, technology has been remarked upon internationally in various fields, especially health care and mental health. Previous researchers described physical activities and sleeping patterns as used methodology to predict the risk of depression, but social avoidance was never minded. Social interaction avoidance can indeed contribute to feelings of depression and exacerbate existing depressive symptoms. In this research, we have proposed an integrated system (wearable device+chatbot and Machine Learning) to detect early depression using a mobile application linked with wearable device APIs that can predict a chance of getting depression by using machine learning, as well as social interaction activities including several interactions with friends or family where we will use chatbot to get some data related to social interactions and emotions. Qualitative-cum-quantitative data collection methods have been considered. The proposed model correctly predicted 0.88% of depressed people with an accuracy of 0.96%, which shows that it can be helpful in depression prevention. As an outcome of this research, there is a benefit from regular personalized well-being advice delivered through this technology, resulting in increased productivity and a stronger sense of purpose as feelings of worthlessness diminish. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI) into the mental health sector marks a significant advancement in healthcare delivery and patient management. This Idea of this paper is to explore the revolutionary potential of AI to transform mental healthcare practices, including diagnostic processes, personalized treatment approaches, and enhancing patient engagement through predictive analytics and virtual support systems. It investigates the transformative effects of AI on the accessibility, affordability, and quality of mental health care by leveraging vast datasets for insightful analysis and real-time decision-making. However, the incorporation of AI technologies within mental health services introduces complex ethical and regulatory challenges, particularly concerning aspects of patient privacy, data security, and the preservation of the doctor-patient relationship. Through a critical examination, this paper aims to contribute to the ongoing dialogue about the ethical application of AI in mental health and to guide the development of responsible AI-driven interventions. The paper also assesses the existing legal frameworks to highlight potential gaps that exist in the burgeoning field of AI-enabled mental healthcare system. [ABSTRACT FROM AUTHOR]

Today's working IT professionals frequently struggle with stress issues. Because of shifting lifestyles and working environments, stress among employees is becoming more likely. The problem is still out of control even though many businesses and organizations offer mental health-related programme and seek to improve the workplace. In this study, we'll study the stress patterns of working individuals and find the factors that have the biggest effects on their stress levels using machine learning approaches. Data from working professionals in the tech industry's replies to the OSMI mental health survey from 2017 was taken into consideration in this regard. Our model was trained using a number of machine learning techniques following meticulous data cleaning and preparation. The preceding information is true models was acquired and comparison analysis was done. Among the models used, Xg-boost gave the most accurate results. Using Decision Trees, it was discovered that Gender, family history, and health benefits availability at work were all major stress-influencing variables. With the help of these findings, businesses may now focus on findings may now focus on finding to make their employees workplaces less stressful and more pleasant. Public target groups including high school kids, college students, and working professionals are employed in this identification procedure. [ABSTRACT FROM AUTHOR]

Even as the number of doctoral students reporting mental health challenges continues to grow, many students who experience significant mental distress do not seek treatment for these concerns. To explore this phenomenon, thirty semi‐structured interviews were conducted with doctoral students at a large U.S. public university. This poster reports on a candidate theme from a larger qualitative analysis that explored the barriers doctoral students face when seeking mental healthcare on‐campus. Insights from the theme "minimal institutional support" are presented here, and discussed with respect to health literacy, information marginalization, and consumer health information justice. [ABSTRACT FROM AUTHOR]

In recent years, there has been a significant shift towards acknowledging mental health challenges due to increased openness among individuals. The World Health Organization (WHO) has stressed the necessity of improving access to quality mental health care services for everyone, regardless of their financial or social status. Global health experts are advocating the need for digital mental health care due to its accessibility and affordability. Despite this growing awareness, there is still a lack of understanding regarding the effectiveness and accessibility of digital mental health care, especially concerning its integration with existing health insurance schemes. This study aims to investigate the role of insurance in the usage of digital mental health services. This study conducted interviews with 25 individuals experiencing emotional distress, using a semi-structured questionnaire. Employing framework analysis, the study found that flexibility was the most significant factor influencing access to digital mental health care, followed by affordability, accessibility, communication, cultural sensitivity, and government subsidies & funding. The study highlighted the crucial role of insurance coverage in facilitating access to these services, suggesting that incorporating mental health insurance is essential for overcoming financial barriers, improving the well-being of insured individuals, and mitigating the societal impact of untreated mental health issues. [ABSTRACT FROM AUTHOR]

Mental illness is a growing public health problem worldwide. The coronavirus disease 2019 (COVID-19) pandemic in early 2020 worldwide exacerbated this mental health problem. The occurrence of the COVID-19 pandemic had a major impact on mental health care and became a turning point for maximizing the use of technology and the internet in the health sector (e-health). The application of good technology will provide short-term and long-term solutions to people's mental health problems. This research aims to design and realize a chatbot model on a mental health consulting information system by applying natural language processing to educate and treat mental health problems, including depression, anxiety, and stress, using the react native framework. This system is based on an expert system algorithm utilizing the forward chaining method. The research results, chatbot model on the mental health consulting information system with the method applied has an optimal level of accuracy, namely the accuracy of the main model formed by 0.98 or 98%, with 92 iterations and an optimal execution time 0.016 seconds. [ABSTRACT FROM AUTHOR]

Data mining has been transitioning to new uses in medicine during the last few years. Many people suffer from mental illnesses as a result of a lack of interest in health care, particularly mental health care. Patients' mental health records are missing for a variety of reasons, including patients' reluctance to see a psychiatrist to follow up on their condition due to the high expense of examination fees, long wait times, and diagnosis error. Predictions are made using classifiers (normal, addiction without disorder, addiction with disorder, or disorder without addiction). As a result, personality disorders and substance misuse are on the rise. To determine the accuracy of the mental disorder prediction system, we employed a dataset of substance abuse patients from the United States and a Convolutional neural network categorization of opioid addicts. The CNN classifier's precision100%. This research describes a new data-mining-based strategy for identifying mental diseases. [ABSTRACT FROM AUTHOR]

Human life in the modern scenario has become quite taxing; mental health issues are on the rise. Our medical science may have progressed well enough to furnish physical health excellency in the general populace (of course, in relative terms), but mental health is still taboo. This is especially so in many of the non-first-world countries, where forthcoming mental health issues and asking for help can lead to anything from public ridicule to social ostracism. In such a scenario, it is of paramount importance that someone comes up with a more private approach to mental health care and well-being. In this paper, the authors discuss how the effects of stress can be monitored using a different implementation of k-NN (k-Nearest Neighbors). With this, using the Manhattan distance metric we were able to achieve 99.84% accuracy, and even after the application of PCA, we achieved 93.97% accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we address the question: how does a monetary incentive influence the empathy expressed in peer users’ and counselors’ responses to questions posted anonymously in online mental health platforms (OMHPs) by individuals suffering from some mental health issue. Rooted in the literature on mental health and empathy and incentives, we hypothesize that a monetary incentive given competitively after passing challenges is likely to increase the expression of cognitive empathy in online responses of all responders in an OMHPs, but the increase in expression of cognitive empathy in online responses will be higher for peer users as compared to counselors. We test our hypotheses using data on 1,455,941 responses to questions from two of the most prominent OMHPs in China in a natural experiment setting after developing a measure of empathy in responses using machine learning methods based on recent approaches from the computer science and medicine fields. Our results provide support to our hypotheses. [ABSTRACT FROM AUTHOR]

Online Health Communities like YouTube offer mental health patients an alternative channel to learn about mental illnesses, the treatment path to follow, and to share their experiences. For many patients who are reluctant to seek professional help, a video on mental health uploaded by a content creator may serve as a substitute for a counsellor. Our work aims to develop an understanding of the relationship between language formality and social support and provide normative guidelines for content creators on social media platforms. Using two transformer-based deep learning classification models, we determine the degree of language formality or informality present in the content, and three dimensions of social support in the comments. We then utilize propensity score estimation to establish the causal effect of (in)formality on the dimensions of social support for 994 videos and 3,10,157 comments. Our findings indicate that informal speech increases emotional support, leading to better health outcomes. [ABSTRACT FROM AUTHOR]

This paper presents a novel artifact called MDscan that can help mental health professionals quickly screen a large number of patients for ten mental disorders. MDscan uses patient responses to the SCL- 90-R clinical questionnaire to create a full-color image, similar to radiological images, which identifies which disorder or combination of disorders may afflict a patient, the severity of the disorder, and the underlying logic of this prediction, using an explainable artificial intelligence (XAI) approach. While prior artificial intelligence (AI) tools have seen limited acceptance in clinical practice because of the lack of transparency and interpretability in their "black box" models, the XAI approach used in MDscan is a "white box" model that elaborates which patient feature contributes to the predicted outcome and to what extent. Using patient data from a mental health clinic, we demonstrate that MDscan outperforms current (expert-based) clinical practice by an average of 20%. [ABSTRACT FROM AUTHOR]

Digital technologies are widely used across the healthcare industry to provide a variety of services. Recently, chatbots, called digital mental health interventions (DMHIs), are increasingly used to provide mental health services to people in need. Given the shortage of mental health professionals needed to provide mental healthcare services, mental health chatbots are increasingly applied to augment these services. Studies indicate multiple response failures in these chatbots, which can lead to discontinuance of use by people in need. However, the nature of response failure and its effect on the discontinuance of mental health chatbots remain understudied. We plan to study this effect and utilize expectation disconfirmation theory and cognitive dissonance theory to guide the development of our propositions that investigate the impact of satisfaction, ease of use, and usefulness on the use discontinuance of mental health chatbots. [ABSTRACT FROM AUTHOR]

The Electronic Health Record system BUPdata served Norwegian Child and Adolescent Mental Health Services (CAMHS) for over 35 years and is still an important source of information for understanding clinical practice. Secondary usage of clinical data enables learning and service quality improvement. We present some insights from explorative data analysis for interpreting the records of patients referred for hyperkinetic disorders. The major challenges were data preparation, preanalysis, imputation, and validation. We summarize the main characteristics, spot anomalies, and detect errors. The results include observations about the patient referral diversity based on 12 different variables. We modeled the activities in an individual episode of care, described our clinical observations among data, and discussed the challenges of data analysis. [ABSTRACT FROM AUTHOR]

My proposed dissertation work highlights social media as digitally-mediated support for neurodivergent individuals. By adopting a critical disability theory lens, I critique the techno-solutionism currently present in digital mental health care. I argue that existing social media platforms can provide community support for neurodivergent individuals to step away from the individualistic approaches currently promoted by much digital mental health technology. These social media-based communities are providing an important service of care and collective knowledge for individuals going through similar experiences to find validation and a sense of agency regarding treatment options. My research will further explore the relationships neurodivergent individuals have had with diagnostic and care systems, as well as ongoing tensions with healthcare providers in both physical and digital spaces. [ABSTRACT FROM AUTHOR]

Digitalization is a macro-trend affecting every sector, albeit in different ways. We study the process of digitalization of the public mental care system, by analyzing the case of the introduction of a new digital technology, Braive, within two Norwegian mental care clinics. Despite positive initial attitudes among the employees, we find that, absent effective managerial and organizational support, professional care providers will effectively resist the adoption of new technologies. Managers need to ensure that professional concerns are heard and integrated within the adoption process, if they intend to reap the potential rewards that digitalization offers for the medical care field. [ABSTRACT FROM AUTHOR]

Since ancient times, a hug has been one of the most basic ways to express emotions and has played an important role in building relationships between people. On the other hand, social robots that are designed to provide mental health care to patients have been attracting great attention, and hugging between humans and robots is becoming more and more popular. In this study, we propose a huggable robot that allows intimate interactions between humans and robots. Our robot is based on a tensegrity structure, which is composed of rigid elements connected by springs, and the structure allows the robot to flexibly respond to external force from a hugging interaction, and express various emotions through its movements. In addition, we conducted user experiments and explored an interaction design for the affective movement of the proposed robot. Through the experiments, we confirm that a robot with a tensegrity structure can be used for a hug interaction and it has a large possibility for emotional interaction. [ABSTRACT FROM AUTHOR]

Psychotherapy which are intended for patients with mental health issues face problems in terms of limited duration and intensity of therapy. This occurs due to several conditions, such as a lack of psychiatric resources, required communication with several multidisciplinary groups, complex procedures, and lack of patient discipline. This study aims to choose the right alternative Internet of Things (IoT) technology that can be implemented in psychotherapy services in psychiatric hospital by taking into account the criteria of Internet of Things implementation. IoT for psychotherapy can help a psychiatrist to provide intensive care with minimal supervision, improve quality measurements with real-time evidence of results, and identify faster recovery. The Best Worst Method (BWM) is used to obtain the weight of each criteria and sub-criteria for applying IoT. The Complex Proportional Assessment Method (COPRAS) is used to determine the priority technology that can be applied. This research results four priority of IoT technology that can be implemented in psychotherapy services in psychiatric hospital. [ABSTRACT FROM AUTHOR]

Recent technological advancements promise new potentials for the use of therapeutic conversational agents (CAs) to deliver mental health interventions. However, both researchers and practitioners face challenges in anthropomorphizing therapeutic CAs for more human-like interactions while avoiding the uncanny feeling from users. Drawing upon the literature of CA anthropomorphism and the uncanny valley theory, the current short paper offers a novel insight into users’ receptivity to therapeutic CAs. It differentiates CAs’ anthropomorphic design features as visual and verbal anthropomorphism and examines their distinct and contrast effects on user perception and advice adherence. We developed a research design to test our model by deploying a 3x2 experiment. We believe this study will provide important contributions that are crucial for the anthropomorphic design of CAs and understanding of the uncanny valley effect in the therapeutic healthcare service setting. [ABSTRACT FROM AUTHOR]

This paper recounts the successful BUPdata, a discontinued electronic health record (EHR) system for Child and Adolescent Mental Health Services (CAMHS) in Norway. It was developed and owned by the national association for CAMHS and fulfilled needs for collaborative care, practice insight, and service management. It aimed to unify the requirements of government, administration, clinicians, patients, and researchers alike, with the goal of providing uniform quality of care nationally. When CAMHS became integrated with specialist healthcare, BUPdata was replaced with more a general EHR system offering far less functionality and insight into CAMHS practice. We have studied BUPdata, and interviewed stakeholders in order to develop decision aids based on practice data analysis and give clinicians and patients insight into successful local practice, collaboration patterns, and overview of local resources. [ABSTRACT FROM AUTHOR]

In recent years the number of individuals struggling with mental illness has increased, and traditional mental health services are now considered insufficient under the current circumstances which has prompted researchers to develop new approaches for mental healthcare. Social media usage is growing, and it is been utilized to help provide additional insight on mental health by using the information shared by individuals, as well as data taken from their social media activity. While this approach may provide a unique and effective perspective for mental health services, it is critical that privacy risks and protections are considered in the process. Social media services collect, process, and stores a substantial amount of information about its users and how that information is shared as well as what type of predictions are made may pose serious privacy concerns. This study aims to understand how privacy is addressed and emphasized during the process of using social media data for mental healthcare by constructing a systematic review on previous scholarly papers related to the topic. Solove's taxonomy of privacy is used to evaluate these publications privacy considerations and to demonstrate the privacy risks that may arise when social media data is used for mental health. [ABSTRACT FROM AUTHOR]

Increasing recognition of youth mental-health problems has created demand for expanded services and more equitable service access. This has put pressure on face-to-face clinical services, leading to growing interest in the efficacy of technology-based mental-health interventions and the values and ethical perspectives that inform these interventions. Previous work has not fully dealt with the range of ethical issues surrounding the use of technology-based therapies, some of which are offered by commercial providers without evidence of efficacy. This raises questions of how to best offer online psychological support. We ask: how can we design and implement online mental-health systems and applications that satisfy the ethical requirements of conventional psychological practice, and what new questions arise in the online environment? We raise several ethical issues that are familiar to healthcare yet distinctive to the technological environment and discuss how we have responded to them in the design of a family of technology-based psychotherapy interventions. Our work is informed by a classic health ethics framework - the four-principle framework - and our contribution is in mapping known problems in eMental health to the categories of this ethical framework. Our analysis will inform the design and use of online treatments as well as future ethical guidelines and regulation. [ABSTRACT FROM AUTHOR]

Individuals with serious mental illness are disproportionately involved in the criminal justice system. In recent years, efforts to reduce justice involvement and recidivism have focused on the implementation of new mental health diversion strategies, as well as increasing access to mental health care during incarceration and upon re-entry. This paper examines the role and experience of mental health professionals working in this context. Specifically, I consider their conceptualization of the relevant institutional logics of safety and care, the occupational challenges associated with their professional prerogatives as mental health 'boundary spanners', and the potential for variation within their perceptions of responsibility of care. Data for this project stem from observations and interviews with mental health professionals working in a local jurisdiction in Texas, with a particular focus on the broad range of mental health services provided by the local mental health authority in coordination with the criminal justice system in that area. [ABSTRACT FROM AUTHOR]

Following a precipitous rise in incarceration rates over the last four decades, U.S. policymakers face increasing pressure to reduce the financial and social costs associated with the criminal justice system. Few proposed reforms have garnered more widespread support than pretrial diversion, which provides for assignment of some individuals to mental health treatment as an alternative to prosecution. Diversion programs are widely hailed as a key solution to the incarceration crisis, but research examining their impacts is limited. Drawing on a year of ethnographic fieldwork in a large urban jurisdiction and analysis of court data, this study has three primary findings. First, although diversion programs allow participants who complete treatment to avoid felony convictions, they can increase punishment for those who do not complete. Second, rates of completion are low and highly unequal across demographic groups. Third, inequalities in completion rates are configured by differences in mental health. As a result, the defendants most in need of mental healthcare are paradoxically those most likely to suffer negative consequences when diverted into treatment. These findings suggest that some therapeutic alternatives may serve less to re-socialize than to sort, setting apart for punishment the racialized and the ill. They raise questions about the risks inherent to reform efforts that repurpose the penal system to deliver mental healthcare rather than simply scaling back the system's reach. [ABSTRACT FROM AUTHOR]

The prevention and treatment of mental health disorders in childhood and adolescence is one among the major public health challenges in the United States today. Prior research has suggested that neighborhood plays an important role in childhood mental health since neighborhood may reduce or induce psychological distresses for children and adolescents. Our study extends the research on neighborhood and mental health by examining the intersectionality of race, built environment in neighborhood, and children's mental health in the U.S. Using data from 2016 National Survey of Children Health (NSCH) and multivariate regression, we find that children's mental health and the neighborhood built environment vary across racial/ethnic groups, with minority groups being more likely to experience mental health disorders and disadvantaged neighborhood. Our study furthers indicates the intersectionality of race/ethnicity, neighborhood built environment and children's mental health, which suggests the need to help reduce the structural disparities in neighborhood for minority groups, particularly American Indians. [ABSTRACT FROM AUTHOR]

Drawing on the frameworks on stigma, labeling theory, and reflected appraisals, this study aims to identify the relationships between race, acculturation, and attitude towards mental health services. Using the Houston Culture and Mental Health Survey (N=2,010), we examine racial differences on mental health services attitude and the impact of race and acculturation on attitudes towards mental health services. Results show that among Asians, Blacks, Latinx, and whites, Asians are the only racial group that were less comfortable seeking mental health services than whites. After controlling for sociodemographic variables and previous medical experience, negative reflected appraisals are associated with less comfortableness in seeking help from a mental health provider. In addition, acculturation had a positive association with perceiving that medicine and counseling is effective, which was in turn positively associated with comfortableness of seeking mental health services. Implications of this study are discussed. [ABSTRACT FROM AUTHOR]

A report released by the Child First Canada in September of 2018 painted a troubling picture of children's health and well-being in Canada. This study specifically gave youth poor marks on a number of health markers including mental health, abuse, poverty and suicide. Using Assessment and Action Record (AAR) data from the Ontario Looking after Children (OnLAC) project, this study investigated the characteristics of youth living in out-of-home care that have the strongest impact on two dimensions of mental health and well-being (i.e., depression, visited a psychiatrist). Results from logistic regressions indicated that youth reporting lower levels of self-control and females were associated with both types of mental health dimensions. Relative to youth in foster care, those placed in group homes were significantly more likely to receive psychiatric services, while the placement type did not influence depression among these youth. These findings suggest a need to broaden the foundation of our understanding of the risk and protective factors of mental illness among this population of youth. That is, while prior research in this area has primarily investigated this topic through a psychological lens, this paper highlights the benefits of incorporating two major theories of criminality, social control theory and the general theory of crime. [ABSTRACT FROM AUTHOR]

Sexual minority men report poorer mental health outcomes and utilize mental health services at higher rates. Existing research has outlined significant barriers to access such as a lack of culturally-competent care. However, the role of power and structural forces - particularly outside of the context of individual-level prejudice - within this phenomenon has been undertheorized. Using Grounded Theory, we analyzed 24 in-depth qualitative interviews conducted with sexual minority men living in Toronto to understand their experiences accessing mental health services. We noted two types of barriers to services. The first were general barriers, obstacles to care not exclusive to sexual or gender minorities such as financial and logistical hurdles, the prominence of psychiatry and the biomedical model, and lackluster provider encounters. The second were sexual and gender minority related barriers, obstacles directly rooted in heterosexism and transphobia, including explicit instances of discrimination and distrust, and limited culturally-competent options. We argue that structural discrimination in mental health care systems reproduces powerlessness and stigma and exacerbates distress. Heath care inaccessibility, precarious employment, and the rising cost of living are the fundamental social conditions fostering adverse mental health outcomes among sexual minority men. Understanding these neoliberal trends helps to explain sustained mental health disparities among this group despite a narrative of increasing social acceptance. We need to expand the scope of what we consider to be pertinent lines of inquiry within sexual minority health research and advocacy, thinking outside of the realms of individual-level interventions toward the structural forces and power relations amplifying mental distress. [ABSTRACT FROM AUTHOR]