This study examined the clinical utility of the recently released National Institute of Mental Health's (NIMH) research domain criteria (RDoC) by replicating and extending earlier work by using a demographically novel sample. Information retrieval and natural language processing of archival clinical records was used to achieve two main objectives: (1) estimate how well the RDoC domains match language used by clinicians by creating domain scores and (2) examine the differences between the DSM's and RDoC's ability to predict treatment outcome using these domain scores and DSM diagnoses. The social systems RDoC category was found to be the strongest predictor of treatment outcome across all diagnostic measures.

Heterogeneous treatment responses are commonly observed in patients with mental disorders. Thus, a universal treatment strategy may not be adequate, and tailored treatments adapted to individual characteristics could improve treatment responses. The theme of the dissertation is to develop statistical and machine learning methods to address patients heterogeneity and derive robust and generalizable individualized treatment strategies by integrating evidence from multi-domain data and multiple studies to achieve precision medicine. Unique challenges arising from the research of mental disorders need to be addressed in order to facilitate personalized medical decision-making in clinical practice. This dissertation contains four projects to achieve these goals while addressing the challenges: (i) a statistical method to learn dynamic treatment regimes (DTRs) by synthesizing independent trials over different stages when sequential randomization data is not available; (ii) a statistical method to learn optimal individualized treatment rules (ITRs) for mental disorders by modeling patients' latent mental states using probabilistic generative models; (iii) an integrative learning algorithm to incorporate multi-domain and multi-treatment-phase measures for optimizing individualized treatments; (iv) a statistical machine learning method to optimize ITRs that can benefit subjects in a target population for mental disorders with improved learning efficiency and generalizability. DTRs adaptively prescribe treatments based on patients' intermediate responses and evolving health status over multiple treatment stages. Data from sequential multiple assignment randomization trials (SMARTs) are recommended to be used for learning DTRs. However, due to the re-randomization of the same patients over multiple treatment stages and a prolonged follow-up period, SMARTs are often difficult to implement and costly to manage, and patient adherence is always a concern in practice. To lessen such practical challenges, in the first part of the dissertation, we propose an alternative approach to learn optimal DTRs by synthesizing independent trials over different stages without using data from SMARTs. Specifically, at each stage, data from a single randomized trial along with patients' natural medical history and health status in previous stages are used. We use a backward learning method to estimate optimal treatment decisions at a particular stage, where patients' future optimal outcome increment is estimated using data observed from independent trials with future stages' information. Under some conditions, we show that the proposed method yields consistent estimation of the optimal DTRs, and we obtain the same learning rates as those from SMARTs. We conduct simulation studies to demonstrate the advantage of the proposed method. Finally, we learn DTRs for treating major depressive disorder (MDD) by stage-wise synthesis of two randomized trials. We perform a validation study on independent subjects and show that the synthesized DTRs lead to the greatest MDD symptom reduction compared to alternative methods. The second part of the dissertation focuses on optimizing individualized treatments for mental disorders. Due to disease complexity, substantial diversity in patients' symptomatology within the same diagnostic category is widely observed. Leveraging the measurement model theory in psychiatry and psychology, we learn patient's intrinsic latent mental status from psychological or clinical symptoms under a probabilistic generative model, restricted Boltzmann machine (RBM), through which patients' heterogeneous symptoms are represented using an economic number of latent variables and yet remains flexible. These latent mental states serve as a better characterization of the underlying disorder status than a simple summary score of the symptoms. They also serve as more reliable and representative features to differentiate treatment responses. We then optimize a value function defined by the latent states after treatment by exploiting a transformation of the observed symptoms based on the RBM without modeling the relationship between the latent mental states before and after treatment. The optimal treatment rules are derived using a weighted large margin classifier. We derive the convergence rate of the proposed estimator under the latent models. Simulation studies are conducted to test the performance of the proposed method. Finally, we apply the developed method to real-world studies. We demonstrate the utility and advantage of our method in tailoring treatments for patients with major depression and identify patient subgroups informative for treatment recommendations. In the third part of the dissertation, based on the general framework introduced in the previous part, we propose an integrated learning algorithm that can simultaneously learn patients' underlying mental states and recommend optimal treatments for each individual with improved learning efficiency. It allows incorporation of both the pre- and post-treatment outcomes in learning the invariant latent structure and allows integration of outcome measures from different domains to characterize patients' mental health more comprehensively. A multi-layer neural network is used to allow complex treatment effect heterogeneity. Optimal treatment policy can be inferred for future patients by comparing their potential mental states under different treatments given the observed multi-domain pre-treatment measurements. Experiments on simulated data and real-world clinical trial data show that the learned treatment polices compare favorably to alternative methods on heterogeneous treatment effects and have broad utilities which lead to better patient outcomes on multiple domains. The fourth part of the dissertation aims to infer optimal treatments of mental disorders for a target population considering the potential distribution disparities between the patient data in a study we collect and the target population of interest. To achieve that, we propose a learning approach that connects measurement theory, efficient weighting procedure, and flexible neural network architecture through latent variables. In our method, patients' underlying mental states are represented by a reduced number of latent state variables allowing for incorporating domain knowledge, and the invariant latent structure is preserved for interpretability and validity. Subject-specific weights to balance population differences are constructed using these compact latent variables, which capture the major variations and facilitate the weighting procedure due to the reduced dimensionality. Data from multiple studies can be integrated to learn the latent structure to improve learning efficiency and generalizability. Extensive simulation studies demonstrate consistent superiority of the proposed method and the weighting scheme to alternative methods when applying to the target population. Application of our method to real-world studies is conducted to recommend treatments to patients with major depressive disorder and has shown a broader utility of the ITRs learned from the proposed method in improving the mental states of patients in the target population.

Indiana University-Purdue University Indianapolis (IUPUI) / Assertive Community Treatment (ACT) is a widely implemented evidence-based practice for consumers with severe mental illness. However, fidelity to the model is variable and program drift, in which programs decrease in fidelity over time, can occur. Given substantial variability in fidelity and program drift in evidence-based practices, a study to examine how to re-implement ACT to high fidelity on established teams was warranted. The present study examined three teams providing moderate fidelity services prior to a state-wide policy change to the definition of ACT. Two of the teams attempted to implement ACT in accordance with state standards, while the third team served as a quasi-control for factors related to other state policy changes, such as a change to the funding mechanism. The implementation effort was examined using qualitative and quantitative measures over a 14-month period at a large, psychosocial rehabilitation center. Themes that were common across all three teams included the perceived negative impact of fee-for-service, ambiguity of stipulations and lack of guidance from the Department of Mental Health (DMH), difficulties with the managed care organization, importance of leadership within the agency, and familiarity with the services. Perceived barriers specific to the implementation of ACT standards included DMH stipulations, staff turnover, lack of resources, and implementation overload, i.e., too many changes at once. One team also had the significant barrier of a misalignment of requirements between two funding sources. Staff attitudes represented both a facilitator and a barrier to ACT implementation, while management being supportive of ACT was viewed as a major facilitator. One of the two teams seeking ACT status was rated at high fidelity within 6 months and maintained high fidelity throughout the study. The other team seeking ACT status never achieved high fidelity and decertified from ACT status after 6 months. The agency’s focus on productivity standards during the implementation effort hampered fidelity on the two teams seeking ACT status and greatly contributed to burnout on all three teams. The team achieving ACT status overcame the barriers in the short-term; however, DMH requirements may have threatened the long-term sustainability of ACT at the agency.

No description available.

This dissertation demonstrates how patients' mental illness treatment careers depend on the change and/or stability among differing levels of social structure. Theorists of the mental illness career tend to ignore the role that higher levels of social structural change have on individuals' mental illness career. Researchers using an organizational perspective tend to focus on the organizational environment but ignore the treatment process from the individual's point of view. Both perspectives neglect what the nation-state's broader socio-political and economic circumstances could imply for people seeking treatment for mental disorders. Organizational theory and theories of the mental illness career are independent theoretical streams that remain separate. This dissertation connects these independent theoretical streams by developing a unifying theoretical framework based on historical analysis. This historical analysis covers three phases of treatment beginning at the end of World War II to the present. This framework identifies mechanisms through which changes in larger levels of social structure can change the experience and career of mental patients. This new perspective challenges current conceptions of the mental illness career as static by accounting for the various levels of social structure that play a part in the mental illness treatment career. Taken together, the inclusion of differing levels of social structure and the subsequent reciprocal relationship between these levels of analysis produce a narrative that explains why and how stability and change within the mental health sector shape the mental illness treatment career. / Ph. D.

No description available.

A research report submitted to the Faculty of Humanities, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Arts in Occupational Social Work, August 2017 / Technology is ubiquitous and presents an opportunity for the psychosocial profession to explore and expand the platforms through which counselling and support for employees is offered. The aim of the research study was to explore the perceptions of counsellors regarding the use of online text technologies as a medium for intervention in the workplace environment. Mobile technology globally and in South Africa is growing rapidly, people are connected to information and services more than ever before. More services are being offered and marketed through the use of online technology mediums; these include professional services for mental wellbeing counselling support. Recognising that this is a relatively new field of study, it is valuable to gain insight from experienced counsellors regarding the use of online text based technologies for counselling specifically for workplace environments. Cultural Historical Activity Theory (CHAT) is used as a framework that it explains how the object of study being text based counselling technologies are used currently characterised by highly mobile social media use. This study employed a qualitative approach and was contextualised to a specific organisation, the South African Depression and Anxiety Group (SADAG) because it offers online counselling interventions which include specific programmes for workplace environments on mental health. Purposive sampling was used to select a total of eight counsellors with experience using online text based mediums and two key informants representing management of the organisation. Individual face-to-face interviews were conducted using two semi- structured interview schedules. Thematic content analysis was used for interpretation of the data. The findings show that counsellors do not prefer to use text based online counselling technologies for serious mental health issues. Results indicate that though online text based technologies are relevant as part of employee wellness services in the workplace, the platforms are more suitable for containment, information and referral purposes. Recommendations from the study are for further research to inform standards of practise and formalised and structured training is required for counsellors. / XL2018

The Community Mental Health Act of 1963 launched the deinstitutionalization movement, whereby individuals with serious mental illnesses were released from psychiatric hospitals and began living and receiving mental health care in the community (Carling, 1995). However, these actions have not necessarily integrated those individuals into all aspects of community life (Dewees, Pulice, & McCormick, 1996). This is unfortunate because people with serious mental illnesses frequently report that community integration is not only important to them, but that it also aids in reducing symptoms and promoting recovery (Townley, 2015). Although past research suggests that receiving mental health care in the community has a positive impact on symptom management, the influence of other community factors (e.g., sense of community, community participation) has yet to be fully explored (Segal, Silverman, & Temkin, 2010). Furthermore, there is lack of understanding as to how these community factors influence other aspects of recovery, such as mental and physical health. As such, the goal of the current study is to better understand the association between community participation and recovery by investigating sense of community as a potential mediating factor between community participation, psychological distress, mental health, and physical health. Data were collected from 300 adults with serious mental illnesses utilizing community mental health services in the United States. Results indicated that sense of community partially mediated the association between community participation and mental health, as well as psychological distress, and fully mediated the association between community participation and physical health. Implications include contributing to the current knowledge base about the role of community factors in recovery and informing future interventions aimed at promoting community integration of adults with serious mental illnesses.

No description available.

M.A. (Clinical Psychology) / The advances in psychopharmacology, as well as the establishment of various 'half-way houses' has changed the nature of psychiatric hospitals. Where once, the emphasis was on long-term, custodial care of patients, the, present emphasis is on short term care. Attempts are made to reintegrate patients into society as soon as possible. This approach has been partially successful. However, it appears that many patients, who were previously discharged as being ready to function and cope with everyday life, return to hospital, having decompensated after relatively short periods of time. The reasons for decompensation are manifold, and vary from individual to individual. It is the contention of this writer that one of the reasons for the limited success of treatment of some patients may lie in the nature of the interdisciplinary team approach to treatment currently operative in many psychiatric hospitals. Interdisciplinary teams are usually composed of psychiatrists, psychologists, social workers, occupational therapists and psychiatric nurses. The teams operate on the tacit assumption that no single approach holds the key to successful treatment of a patient...