Subgroups of non-suicidal self-injury in a large diverse sample of online help-seekers DOI Creative Commons
Kaylee Payne Kruzan, Jason J. Washburn, David Aaby

et al.

Frontiers in Psychiatry, Journal Year: 2025, Volume and Issue: 16

Published: Feb. 27, 2025

Many young people access information and resources for nonsuicidal self-injury (NSSI) online; yet our understanding of who accesses such is limited. NSSI a behavior with varied presentations. Understanding heterogeneity can help guide person-centered intervention. The present study aimed to (1) empirically identify classes individuals (2) compare the according demographic clinical characteristics. Data were collected from survey posted national advocacy group website. Latent class analysis was used derive based on characteristics associated severity. Relationships between latent variables along five dimensions (behavior change, consequences or life interference, expectancies, functions, across lifetime) explored via logistic regression models. 11,262 reporting past month included in analyses. 4-class model provided most clinically interpretable groups. Class 1 smallest (16.8%), scored highest all items reported youngest age onset. 3 largest (31.8%), lowest latest Classes 2 (29.3%) 4 (22.2%) had moderate scores differed levels suicidal ideation. presented more severe symptoms than what typical samples extant literature underscoring importance tailoring interventions dissemination online contexts.

Language: Английский

Subgroups of non-suicidal self-injury in a large diverse sample of online help-seekers DOI Creative Commons
Kaylee Payne Kruzan, Jason J. Washburn, David Aaby

et al.

Frontiers in Psychiatry, Journal Year: 2025, Volume and Issue: 16

Published: Feb. 27, 2025

Many young people access information and resources for nonsuicidal self-injury (NSSI) online; yet our understanding of who accesses such is limited. NSSI a behavior with varied presentations. Understanding heterogeneity can help guide person-centered intervention. The present study aimed to (1) empirically identify classes individuals (2) compare the according demographic clinical characteristics. Data were collected from survey posted national advocacy group website. Latent class analysis was used derive based on characteristics associated severity. Relationships between latent variables along five dimensions (behavior change, consequences or life interference, expectancies, functions, across lifetime) explored via logistic regression models. 11,262 reporting past month included in analyses. 4-class model provided most clinically interpretable groups. Class 1 smallest (16.8%), scored highest all items reported youngest age onset. 3 largest (31.8%), lowest latest Classes 2 (29.3%) 4 (22.2%) had moderate scores differed levels suicidal ideation. presented more severe symptoms than what typical samples extant literature underscoring importance tailoring interventions dissemination online contexts.

Language: Английский

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