Evidence map›Paper›PMID 37676700›Full record

ArticleJMIR formative research2023

Predicting Youth and Young Adult Treatment Engagement in a Transdiagnostic Remote Intensive Outpatient Program: Latent Profile Analysis.

Kate Gliske, Katie R Berry, Jaime Ballard, Clare Schmidt, Elizabeth Kroll, Jonathan Kohlmeier, Michael Killian, Caroline Fenkel

Abstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Kate GliskeCharlie Health Inc, Bozeman, MT, United States.ORCID https://orcid.org/0000-0001-6109-3624
Katie R BerryCharlie Health Inc, Bozeman, MT, United States.ORCID https://orcid.org/0000-0001-8340-3259
Jaime BallardCenter For Applied Research and Educational Improvement, University of Minnesota, St. Paul, MN, United States.ORCID https://orcid.org/0000-0002-2506-9034
Clare SchmidtCharlie Health Inc, Bozeman, MT, United States.ORCID https://orcid.org/0000-0002-4493-5591
Elizabeth KrollCharlie Health Inc, Bozeman, MT, United States.ORCID https://orcid.org/0009-0008-1840-3939
Jonathan KohlmeierCharlie Health Inc, Bozeman, MT, United States.ORCID https://orcid.org/0000-0002-3571-8210
Michael KillianCollege of Social Work, Florida State University, Tallahassee, FL, United States.ORCID https://orcid.org/0000-0002-2287-9007
Caroline FenkelCharlie Health Inc, Bozeman, MT, United States.ORCID https://orcid.org/0000-0003-0601-9020

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

objectiveThis 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.

methodsData 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.

resultsAmong 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 (ꭓ

conclusionsThis 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.

Indexed as

intensive outpatient treatmentlatent profile analysismental healthpersonalized treatmentvirtualyoung adultyouth

Identifiers

PMID37676700
PMCPMC10514771

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.