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Record W4361858455 · doi:10.2196/45509

Understanding Treatment Needs of Youth in a Remote Intensive Outpatient Program Through Solicited Journals: Quality Improvement Analysis

2023· article· en· W4361858455 on OpenAlexvenueno aff
Michelle Evans‐Chase, Rachel Kornmann, Bethany Peralta, Kate Gliske, Katherine A. Berry, Phyllis Solomon, Caroline Fenkel

Bibliographic record

VenueJMIR Formative Research · 2023
Typearticle
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisMedicineCoding (social sciences)PopulationIntervention (counseling)Mental healthInclusion (mineral)Family medicineNursingPsychologyMedical educationPsychiatryQualitative research

Abstract

fetched live from OpenAlex

BACKGROUND: Youth experiencing high-acuity mental health symptoms often require highly restrictive levels of care (ie, inpatient care) that removes them from the relationships and activities essential for healthy development. An alternative treatment gaining evidence in its ability to support this population is the intensive outpatient programming (IOP) model. Understanding the experiences of adolescents and young adults during IOP treatment episodes may enhance clinical responsiveness to changing needs and protect against transfer to inpatient care. OBJECTIVE: The objective of the analysis reported here was to identify heretofore unrecognized treatment needs of adolescents and young adults attending a remote IOP to help the program make clinical and programmatic decisions that increase its ability to support the recovery of program participants. METHODS: Treatment experiences are collected weekly via electronic journals as part of ongoing quality improvement efforts. The journals are used by clinicians proximally to help them identify youth in crisis and distally to help them better understand and respond to the needs and experiences of program participants. Journal entries are downloaded each week, reviewed by program staff for evidence of the need for immediate intervention, and later deidentified and shared with quality improvement partners via monthly uploads to a secure folder. A total of 200 entries were chosen based on inclusion criteria that focused primarily on having at least one entry at 3 specified time points across the treatment episode. Overall, 3 coders analyzed the data using open-coding thematic analysis from an essentialist perspective such that the coders sought to represent the data and thus the essential experience of the youth as closely as possible. RESULTS: Three themes emerged: mental health symptoms, peer relations, and recovery. The mental health symptoms theme was not surprising, given the context within which the journals were completed and the journal instructions asking that they write about how they are feeling. The peer relations and recovery themes provided novel insight, with entries included in the peer relations theme demonstrating the central importance of peer relationships, both within and outside of the therapeutic setting. The entries contained under the recovery theme described experience of recovery in terms of increases in function and self-acceptance versus reductions in clinical symptoms. CONCLUSIONS: These findings support the conceptualization of this population as youth with both mental health and developmental needs. In addition, these findings suggest that current definitions of recovery may inadvertently miss supporting and documenting treatment gains considered most important to the youth and young adults receiving care. Taken together, youth-serving IOPs may be better positioned to treat youth and assess program impact through the inclusion of functional measures and attention to fundamental tasks of the adolescent and young adult developmental periods.

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)0.079
metaresearch head score (Gemma)0.196
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 score0.079
Threshold uncertainty score0.418

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0790.196
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0140.016
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0020.005
Research integrity0.0010.001
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 head0.663
GPT teacher head0.628
Teacher spread0.034 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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