An Idionomic Network Analysis of Psychological Processes and Outcomes

Background: Clinical psychology research emphasizing treatment packages targeted at DSM defined problems obscures individual differences and violates statistical assumptions regarding its applicability to individuals in the sample. An alternative approach maps the relationship between psychological processes and outcomes at the individual level before aggregating results. This study represents the first effort to undertake such an approach using a novel measure, the Process Based Assessment Tool (PBAT), that assesses functionally defined psychological processes linked to intervention and based on modern evolution science. Methods: Data on psychological variation, selection, and retention, domains of psychological distress, life satisfaction, and burnout, were collected twice daily for a 35day period using a smartphone application. These data were analyzed using the SGIMME statistical package to generate group, sub-group, and individual level network models. Results: S-GIMME models successfully converged for all participants. Network models directed at each of 7 outcomes yielded interpretable subgroups. Elements of the PBAT reliably produced directed pathways impacting elements of psychological distress within the sample. 17 of 18 elements of the PBAT appear in final models which maximized directed pathways toward each of the 7 targeted outcomes. Discussion: The PBAT demonstrated utility as a daily diary measure and reliably produced directed pathways impacting domains of psychological distress and well-being. Subgroup formation demonstrated consistency across outcomes directed models. Individual network models represent potential clinical utility

The role of the individual in the coming era of process-based therapy

For decades the development of evidence-based therapy has been based on experimental tests of protocols designed to impact psychiatric syndromes. As this paradigm weakens, a more process-based therapy approach is rising in its place, focused on how to best target and change core biopsychosocial processes in specific situations for given goals with given clients. This is an inherently more idiographic question than has normally been at issue in evidence-based therapy over the last few decades. In this article we explore methods of assessment and analysis that can integrate idiographic and nomothetic approaches in a process-based era.Accepted manuscrip

Patterns and predictors of smoking by race and medical diagnosis during hospital admission: A latent class analysis

Hospital-based tobacco treatment programs provide tobacco cessation for a diverse array of admitted patients. Person-centered approaches to classifying subgroups of individuals within large datasets are useful for evaluating the characteristics of the sample. This study categorized patients who received tobacco treatment while hospitalized and determined whether demographics and smoking-related health conditions were associated with group membership. Chart review data was obtained from 4854 patients admitted to a large hospital in South Carolina, USA, from July 2014 through December 2019 who completed a tobacco treatment visit. Smoking characteristics obtained from the visit interview were dichotomized, and then latent class analysis (LCA) was conducted to categorize patients based on smoking history and interest in stopping smoking. Finally, logistic regressions were used to evaluate demographics and smoking-related health conditions as predictors of class membership. LCA generated 5 classes of patients, differentiated by heaviness of smoking and motivation to quit. Patients who were black/African American were more likely to be lighter smokers compared to white patients. Hospitalized patients with a history of hypertension, diabetes, and congestive heart failure were more likely to be motivated to quit and also were more likely to be lighter smokers at the time of hospitalization. Hospitalized patients who smoke and receive tobacco treatment are heterogeneous in terms of their smoking histories and motivation to quit. Understanding latent categories of patients provides insight for tailoring interventions and potentially improving tobacco treatment outcomes

Conservative management of lowâ risk prostate cancer among young versus older men in the United States: Trends and outcomes from a novel national database

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151903/1/cncr32332.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151903/2/cncr32332_am.pd

Modern psychotherapy as a multidimensional multilevel evolutionary process

Modern evidence-based psychotherapy can be understood by consideration of six key concepts from evolution science that have an impact on behavioral science: variation, selection, retention, dimensions, levels, and context. Human behavior problems are most likely to emerge when repertoires are narrow or rigid, are under inappropriate selection criteria targeted at the wrong level or dimension, and without retention of successful variants that are fitted to context. Modern effective psychotherapies represent the inverse process of creating broad and flexible repertoires, selected by personal values, and fitted to particular contexts at the appropriate level and in the right dimension. Psychotherapy can thus be considered an applied evolution science

Toward empirical process-based case conceptualization : An idionomic network examination of the process-based assessment tool

Background: Syndromal classification has failed to produce a progressive science of case conceptualization for mental and behavioral health issues. An idiographic application of processes of change can provide a viable empirical functional analytic alternative if it could be linked to an idionomic approach, modeling idiographic effects first, and retaining nomothetic findings if they improve idiographic fit. Method: The present study examined this possibility by using the Process-Based Assessment Tool (PBAT), a new assessment tool linked to the Extended Evolutionary Meta-Model (EEMM) of Process-Based Therapy. The PBAT and items assessing common clinical outcomes were assessed repeatedly in 50 individuals in an experience sampling format over a 35 day period yielding at least 60 measurement occasions per person. These data were then analyzed in an idionomic fashion using Group Iterative Multiple Model Estimation (GIMME). Results: Analyses showed that the PBAT related to common clinical outcomes for virtually all participants in the individual complex networks identified by GIMME. Data showed that relationships had to be studied using an idionomic approach because participants’ responses violated the ergodic assumptions underlying classical normative statistics. No overall group patterns were found. Subgroup relations did emerge for three common outcomes (sadness, anxiety, and life satisfaction) but most process to outcome relationships were idiographic. Idiographic networks were interpretable, however, using the broadened psychological flexibility approach of the EEMM. Conclusion: Idionomic network analysis of processes of change may provide a replicable form of empirical functional analysis and process-based case conceptualization