Avolition in Early Psychosis: Internal Experience, Temporal Dynamics, and Relationships with Environmental Factors
Bibliographic record
Abstract
Schizophrenia-spectrum disorders (SSD) are serious mental health conditions associated with significant health, social, and economic concerns. SSD affect millions of people in Canada and are very difficult to treat effectively. As a result, individuals with SSD have substantially reduced life expectancy and experience high rates of unemployment, homelessness, and psychiatric problems such as depression, suicide, and substance use. Recent Canadian mental health strategies have emphasized the importance of functional recovery in SSD, yet modern interventions have done little to improve recovery rates. To do so, we must improve the mechanistic understanding of the factors driving functional disability in these complex disorders. Motivation is a psychological process crucial for initiating and persisting in tasks and activities, completing goals, and healthy community functioning overall. Motivational impairment (MI) is considered one of the hallmark symptoms of SSD. Research has shown that they are strongest determinant of everyday functioning in this population and are associated with prolonged illness course and lower levels of recovery. Unfortunately, Mis are among the hardest symptoms to measure and treat as much remains unknown about how they manifest and affect behaviour. Ecological momentary assessment (EMA) is an exciting new avenue for capturing motivation in a dynamic way. EMA refers to the real-time collection of data in everyday life, most commonly using mobile devices. It allows for a more natural and nuanced evaluation of thoughts, feelings, and behaviours in everyday life and in the individual’s own environment. This offers a unique look into the fluctuations of internal experience, providing a more ecologically valid and nuanced measure than an interview or task completed at a single time point. One notable gap in the way MI has been conceptualized in the research has to do with its variability. It has largely been described as a stable phenomenon, translating to the view that MIs are static. We believe they are much more complex and variable than previously believed, but there has been very little empirical investigation of dynamic fluctuations of motivation across contexts. To determine whether a behaviour is a direct consequence of the internal state of motivation, this temporal relationship must be investigated. Another gap in the understanding of motivation is related to socio-environmental factors. We believe there are important socio-environmental processes which cause and maintain MI that have yet to be empirically evaluated. For example, lower socio-economic status, under-stimulating physical environments, urbanicity, and smaller social networks have all been proposed as potential contributors. Thus, this study will examine internal and external factors related to MI in the daily lives of people with SSD. We will use EMA to assess whether engagement in goal-directed activities occurs as a result of the internal state of motivation by surveying participants about their present experience of motivation for daily activities throughout the day. The use of EMA technology also enables us to simultaneously collect information on social network, material resource metrics, and the physical environment via global positioning system to determine whether the relationship between motivation and goal-directed activity is moderated by socio-environmental factors. Hierarchical linear modeling will be used to analyze these multi-level data. There are theoretical, methodological, and clinical implications of this research, as the internal experience of motivation is not well understood in SSD. If the underlying construct of avolition is more dynamic than presently believed, this will call into question the construct validity of many existing motivation measures and can be used to inform better psychometric tools. In addition, if socio-environmental factors exert a significant effect on the relationship between motivation and goal-directed behaviour, our treatments could be vastly improved by integrating and accounting for such factors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".