A Study On Disagreements Between Patients And Psychiatrists, Their Nature, Type, Contributing Variables, And Consequences

This research identified disagreements between patients and psychiatrists on problems, goals, and methods for treatment. Specifically, their nature, type, contributing variables, and consequences were investigated.;The study was conducted at two provincial psychiatric hospitals in Ontario. Subjects included patients (n = 135) diagnosed as depressed, manic, neurotic, or schizophrenic. The attending psychiatrist (n = 29) also participated in the study.;Two to five days after admission to hospital, patients were interviewed according to a checklist on problems, goals, and methods for treatment. At the same time, the psychiatrist completed an identical checklist. Patients were observed for discharge against medical advice (AMA) and absent without leave (AWOL) within the first six weeks of hospitalization.;Findings pertaining to disagreements on problems, goals, and methods for treatment, of an environmental and psychological nature, were consistently of the type whereby psychiatrists identified items when their patients did not. In addition, the relationship between patient variables; psychiatrist variables; variables related to both the patient and psychiatrist; and disagreements were examined. Few of these variables, with the exception of involuntary detainment, were found to be associated with disagreements. There was a strong relationship between involuntary detainment and disagreements on problems, goals, and methods for treatment. Also, a significant relationship was found between disagreements and the likelihood of patient discharge AMA or AWOL.;In summary, disagreements were of the type whereby the psychiatrist identified problems, goals, and methods for treatment, when the patient did not. A consequence of these disagreements was found to be patient discharge AMA or AWOL. The above findings have implications for clinical practice. Awareness of disagreements would enable the psychiatrist to attempt appropriate interventions to prevent or mitigate adverse consequences. This, in turn, would provide for the effective management of psychiatric patients

Recognition of loneliness as a basis for psychotherapy

This study on the recognition of loneliness as a basis for psychotherapy developed a conceptual model for loneliness intervention. Specific loneliness behaviours and suggested loneliness interventions to be implemented during psychotherapy were identified in the conceptual model for loneliness intervention. The review of the literature supported the need for research on conceptualizing loneliness to facilitate psychotherapy with lonely clients. A quasi-experimental design was employed in the study. The Schmidt-Sermat Loneliness Scale was utilized to identify clients who tested high in loneliness. In Part I of the study, the control group, thirteen mental health clients who tested high in loneliness were involved in psychotherapy with one of four therapists. 1 Upon completion of six psychotherapy sessions, the clients were again tested for loneliness. An Inservice Education on loneliness and an explanation of the implementing of the conceptual model for loneliness intervention during psychotherapy, as developed by the investigator, was given. Specific loneliness behaviours and possible loneliness interventions were inherent in the model. A new group of eleven clients who tested high in loneliness were identified to the same four therapists who participated in Part I of the study. These clients formed the comparison group for Part II of the study. Loneliness consultation was provided on a weekly basis by the investigator to facilitate therapist implementation of the conceptual model for loneliness intervention. Clients were again tested for loneliness after six therapy sessions. At the end of Part I and Part II, therapists rated their perception of progress in psychotherapy and satisfaction in attempting loneliness intervention. Open end-interviews on the implementation of the conceptual model for loneliness intervention was also conducted. Analysis of the findings of the study resulted in Hypotheses I, II, and III being upheld. Psychotherapy was more effective in reducing loneliness when the conceptual model for loneliness intervention was implemented. Therapists who utilized loneliness intervention with clients who tested high in loneliness found the psychotherapy sessions more satisfying. Therapist perception of client progress in psychotherapy increased when the conceptual model for loneliness intervention was implemented. The findings of the study were strongly significant and indicated the usefulness of a conceptual model for loneliness intervention. The primary recommendation of the study was that loneliness psychotherapy be conducted with mental health clients who are lonely. The presentation of loneliness as a basis for psychotherapy requires that the concept of loneliness be theoretically and conceptually defined. Basically, the study recommended that there be further exploration of the concept of loneliness in the field of mental health. For further research, it was suggested that this research be conducted in a hospital setting on a psychiatric ward where on-going therapy is conducted on a daily basis. This would allow for the facilities at the hospital to be readily integrated with the loneliness interventions which would involve therapists to directly observe and participate in the loneliness interventions, in a role-model situation, if appropriate. Individuals have always experienced loneliness, many have suffered from this feeling. It is the inherent goal of health professionals to promote mental health. By setting a sound base for loneliness in psychotherapy, mental health care may be improved. This can also be achieved by therapists, educators, and researchers furthering the knowledge and conceptualization of loneliness to form a strong theoretical base for this concept.Applied Science, Faculty ofNursing, School ofGraduat

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R2 increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase

Genetic influences on schizophrenia and subcortical brain volumes:Large-scale proof of concept

Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders