Editorial: Advances in identifying individuals at clinical high risk (CHR) for psychosis: perspectives from North America
Notice bibliographique
Résumé
The five articles contributing to this Research Topic address three timely interconnected themes: 1) Culturally-and environmentally-informed symptom assessment, diagnosis, and treatment, 2) The relationship between exposure to contextual risk factors, stress, and clinical presentation, and translation into treatment development and delivery, and 3) Stress management, coping skills, and social functioning as targets for intervention. Bridgwater et al. (2023), Devoe et al. (2023), andZarubin et al. (2023) all examine the role of contextual factors in risk identification and symptom presentation. Bridgewater and colleagues argue that the inclusion of contextual factors in risk assessment is essential for accurate, unbiased determination of vulnerability to psychosis, and excluding these factors may lead to assessment bias and misdiagnosis. They present a narrative review of eight contextual factors relevant to CHR assessment in North American populations: race/ethnic identity, experience of discrimination, neighborhood context, trauma exposure, immigration status, gender identity, sexual orientation, and age. Taking each factor in turn, Bridgewater et al. present current research addressing contextual effects on risk of psychosis. The authors then offer practical clinical guidance for incorporating these contextual factors into assessment protocols.CHR youth are highly vulnerable to the emotional and physiological effects of stress, including environmental stressors, interpersonal stress, and trauma. Stress exposure exacerbates CHR symptoms (Muñoz-Samons et al., 2021), and physiological changes in response to stress have been shown to precede psychosis onset (Holtzman et al., 2013). Experience of trauma is reported by the majority of CHR youth (Mayo et al., 2017), including childhood, environmental, and systemic sources of trauma. Given the consequences of stress exposure, it is not surprising that a history of trauma is associated with a greater risk of transition to psychosis among CHR youth (Georgiades et al., 2023). In their perspective piece, Zarubin and colleagues focus on the characterization, assessment, and treatment of trauma in CHR youth, including key and often overlooked considerations regarding developmental timing, frequency, and intensity of trauma exposure. They note that trauma can come from multiple and often overlapping sources, including childhood trauma, exposure to crime and violence, population density, and poverty, and discuss the unique needs of CHR youth versus adults with schizophrenia.Devoe and colleagues examine how early contextual/environmental exposure and premorbid adjustment may relate to the clinical presentation of CHR youth. In their article, Devoe et al. focus on persistent negative symptoms (PNS) in CHR youth, specifically social anhedonia, avolition, and decreased expression of emotion. They present original research examining premorbid adjustment, life events, history of trauma, bullying, cannabis use, and emergency or inpatient treatment utilization in CHR youth with and without PNS. Primary results show significantly lower child and adolescent premorbid adjustment among the sample of CHR youth with PNS versus the non-PNS CHR sample. Additionally, worse premorbid adjustment in late adolescence predicts PNS independent of other variables. These results are consistent with the substantial evidence that low social functioning and limited connection with peers, particularly during late adolescence, predicts greater symptom severity and higher risk for transition to psychosis (Cornblatt et al., 2012).Late adolescence through early adulthood is a period of consequential neurodevelopmental change (Benes, 2003) and increased exposure to instrumental and interpersonal stress. As noted by Gupta et al. (2023) and Bargiota et al. (2023), this period offers an opportune window for intervention, yet as cautioned by Zarubin et al., also presents unique challenges such as changing environmental stressors and shifting treatment targets.Treatments aimed at mitigating the impact of exposure to environmental risk factors and improving clinical outcome in CHR youth are being developed and tested. Gupta and colleagues introduce the Skills Program for Awareness, Connectedness, and Empowerment (SPACE) that they are currently developing for CHR youth ages 13-18. This program targets specific sources of functional impairment and heightened distress in CHR youth that are thought to contribute to symptom progression. The 21-week skills group is organized into three successive stages building on the skills developed during the previous stage(s): 1) coping and stress management, 2) self-concept and identity formation, and 3) interpersonal connectedness and communication.Bargiota and colleagues examine a neurophysiological-based approach to enhance social cognition in CHR youth utilizing intranasal oxytocin (OT). Bargiota et al. review six studies presenting results of five randomized controlled trials that investigated effects of intranasal OT in CHR or early psychosis adolescents and adults. Four studies (three trials) focused on CHR samples, with results indicating OT-induced changes in brain activation during completion of social cognition tasks and changes in autonomic activation (stress response). These results suggest that oxytocin may affect processes relevant to social cognition and stress sensitivity in CHR youth, but as Bargiota et al. point out, substantial work is needed before impact on clinical symptoms is known.In the field of early identification and intervention in psychosis, there is increasing emphasis on the role of contextual factors in risk of psychosis and their importance for achieving the goals of improving early identification, decreasing symptom severity and distress, and minimizing risk of psychosis. Together, the articles included in this research topic examine the nature and impact of contextual factors, consider issues of assessment and characterization, and discuss contextinformed treatment strategies.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,016 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,007 |
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 source (Gemma direct ou Codex distillé), 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 ».