Avolition in Early Psychosis: Internal Experience, Temporal Dynamics, and Relationships with Environmental Factors
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».