Predicting Youth and Young Adult Treatment Engagement in a Transdiagnostic Remote Intensive Outpatient Program: Latent Profile Analysis
Notice bibliographique
Résumé
Background The youth mental health crisis in the United States continues to worsen, and research has shown poor mental health treatment engagement. Despite the need for personalized engagement strategies, there is a lack of research involving youth. Due to complex youth developmental milestones, there is a need to better understand clinical presentation and factors associated with treatment engagement to effectively identify and tailor beneficial treatments. Objective This quality improvement investigation sought to identify subgroups of clients attending a remote intensive outpatient program (IOP) based on clinical acuity data at intake, to determine the factors associated with engagement outcomes for clients who present in complex developmental periods and with cooccurring conditions. The identification of these subgroups was used to inform programmatic decisions within this remote IOP system. Methods Data were collected as part of ongoing quality improvement initiatives at a remote IOP for youth and young adults. Participants included clients (N=2924) discharged between July 2021 and February 2023. A latent profile analysis was conducted using 5 indicators of clinical acuity at treatment entry, and the resulting profiles were assessed for associations with demographic factors and treatment engagement outcomes. Results Among the 2924 participants, 4 profiles of clinical acuity were identified: a low-acuity profile (n=943, 32.25%), characterized by minimal anxiety, depression, and self-harm, and 3 high-acuity profiles defined by moderately severe depression and anxiety but differentiated by rates of self-harm (high acuity+low self-harm: n=1452, 49.66%; high acuity+moderate self-harm: n=203, 6.94%; high acuity+high self-harm: n=326, 11.15%). Age, gender, transgender identity, and sexual orientation were significantly associated with profile membership. Clients identified as sexually and gender-marginalized populations were more likely to be classified into high-acuity profiles than into the low-acuity profile (eg, for clients who identified as transgender, high acuity+low self-harm: odds ratio [OR] 2.07, 95% CI 1.35-3.18; P<.001; high acuity+moderate self-harm: OR 2.85, 95% CI 1.66-4.90; P<.001; high acuity+high self-harm: OR 3.67, 95% CI 2.45-5.51; P<.001). Race was unrelated to the profile membership. Profile membership was significantly associated with treatment engagement: youth and young adults in the low-acuity and high-acuity+low–self-harm profiles attended an average of 4 fewer treatment sessions compared with youth in the high-acuity+moderate–self-harm and high-acuity+high–self-harm profiles (ꭓ23=27.6, P<.001). Individuals in the high-acuity+low–self-harm profile completed treatment at a significantly lower rate relative to the other 2 high-acuity profiles (ꭓ23=13.4, P=.004). Finally, those in the high-acuity+high–self-harm profile were significantly less likely to disengage early relative to youth in all other profiles (ꭓ23=71.12, P<.001). Conclusions This investigation represents a novel application for identifying subgroups of adolescents and young adults based on clinical acuity data at intake to identify patterns in treatment engagement outcomes. Identifying subgroups that differentially engage in treatment is a critical first step toward targeting engagement strategies for complex populations.
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 | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».