{"doi":"10.31234/osf.io/nz2ms","title":"Predicting the Trajectory of Non-suicidal Self-injury Among Adolescents","abstract":"Objective: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a high-risk period for self-injurious behavior. Thus, identifying predictors that shape the course of post-discharge NSSI may provide insights into ways to improve clinical outcomes. Accordingly, we used machine learning to identify the strongest predictors of NSSI trajectories drawn from a comprehensive clinical assessment. Method: The study included adolescents (N=612; females n=435; 71.1%) aged 13-19-years-old (M=15.6, SD=1.4) receiving treatment from a psychiatric inpatient service. Youth were administered clinical interviews and symptom questionnaires at treatment initiation (baseline) and before termination. NSSI frequency was assessed at one-, three-, and six-month follow-ups. Latent class growth analyses were used to group adolescents based on their pattern of NSSI engagement across follow-ups. Results: Three classes were identified, reflecting: Low Stable (n=83), Moderate Fluctuating (n=260), and High Persistent (n=269). Important predictors of the High Persistent class in our regularized regression models (LASSO) included baseline psychiatric symptoms and comorbidity, past-week suicidal ideation (SI) severity, lifetime average and worst-point SI intensity, and NSSI in the past 30 days (bs=0.75-2.33). Only worst-point lifetime suicide ideation intensity was identified as a predictor of the Low Stable class (b=-8.82); no predictors of Moderate Fluctuating class were identified. Conclusion: This study identified a set of intake clinical variables that indicate which adolescents may experience persistent NSSI post-discharge. Accordingly, this may help identify the youth that may benefit from additional monitoring and support post-hospitalization.","journal":"PsyArXiv (OSF Preprints)","year":2023,"id":397312,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9623,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":367040,"name":"Randy P. Auerbach","orcid":"0000-0003-2319-4744","position":1,"is_corresponding":false},{"id":610021,"name":"Jeremy G. Stewart","orcid":"0000-0001-6927-6871","position":2,"is_corresponding":false},{"id":1172909,"name":"Geneva Mason","orcid":"0000-0002-9924-5889","position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":null,"created_at":"2026-07-19T01:19:35.497854Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}