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Record W4308772363 · doi:10.2196/41721

Mental Health Outcomes for Youths With Public Versus Private Health Insurance Attending a Telehealth Intensive Outpatient Program: Quality Improvement Analysis

2022· article· en· W4308772363 on OpenAlexvenueno aff
Kate Gliske, Katherine A. Berry, Jaime Ballard, Michelle Evans‐Chase, Phyllis Solomon, Caroline Fenkel

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMcNemar's testMedicaidMental healthTelehealthPublic healthFamily medicineHealth careGerontologyPsychiatryTelemedicineNursing

Abstract

fetched live from OpenAlex

Background COVID-19 exacerbated a growing mental health crisis among youths and young adults, worsened by a lack of existing in-person options for high-acuity care. The emergence and growth of remote intensive outpatient programs (IOPs) is a solution to overcome geographic limitations to care. However, it remains unclear whether remote IOPs engender equivalent clinical outcomes among youths with public insurance (eg, Medicaid) versus private insurance (eg, commercial) given the disparities found in previous research on place-based treatment in both clinical and engagement outcomes. Objective This analysis sought to establish, as part of ongoing quality improvement efforts, whether engagement and clinical outcomes among adolescents and young adults attending remote IOP treatment differed between youths with public and those with private insurance. The identification of disparities by payer type was used to inform programmatic decisions within the remote IOP system for which this quality improvement analysis was conducted. Methods Pearson chi-square analyses and independent 2-tailed t tests were used to establish that the 2 groups defined by insurance type were equivalent on clinical outcomes (depression, suicidal ideation, and nonsuicidal self-injury [NSSI]) at intake and compare changes in clinical outcomes. McNemar chi-square analyses and repeated-measure 2-tailed t tests were used to assess changes in clinical outcomes between intake and discharge in the sample overall. In total, 495 clients who attended the remote IOP for youths and young adults in 14 states participated in ≥7 treatment sessions, and completed intake and discharge surveys between July 2021 and April 2022 were included in the analysis. Results Overall, the youths and young adults in the remote IOP attended a median of 91% of their scheduled group sessions (mean 85.9%, SD 16.48%) and reported significantly fewer depressive symptoms at discharge (t447=12.51; P<.001). McNemar chi-square tests of change indicated significant reductions from intake to discharge in suicidal ideation (N=470, χ21=104.4; P<.001), with nearly three-quarters of youths who reported active suicidal ideation at intake (200/468, 42.7%) no longer reporting it at discharge (142/200, 71%), and in NSSI (N=430, χ21=40.7; P<.001), with more than half of youths who reported NSSI at intake (205/428, 47.9%) reporting lower self-harm at discharge (119/205, 58%). No significant differences emerged by insurance type in attendance (median public 89%, median private 92%; P=.10), length of stay (t416=−0.35; P=.73), or reductions in clinical outcomes (depressive symptom severity: t444=−0.87 and P=.38; active suicidal ideation: N=200, χ21=0.6 and P=.49; NSSI frequency: t426=−0.98 and P=.33). Conclusions Our findings suggest that youths and young adults who participated in remote IOP had significant reductions in depression, suicidal ideation, and NSSI. Given access to the same remote high-acuity care, youths and young adults on both public and private insurance engaged in programming at comparable rates and achieved similar improvements in clinical outcomes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.180
GPT teacher head0.524
Teacher spread0.344 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

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