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Record W4399733834 · doi:10.2196/54816

Using a Digital Mental Health Intervention for Crisis Support and Mental Health Care Among Children and Adolescents With Self-Injurious Thoughts and Behaviors: Retrospective Study

2024· article· en· W4399733834 on OpenAlexvenueno aff
Darian Lawrence‐Sidebottom, Landry Goodgame Huffman, Aislinn Beam, Kelsey McAlister, Rachael Guerra, Amit Parikh, Monika Roots, Jennifer Huberty

Bibliographic record

VenueJMIR Formative Research · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthIntervention (counseling)Crisis interventionMental health carePsychologyPsychiatryMedicineClinical psychology

Abstract

fetched live from OpenAlex

Background Self-injurious thoughts and behaviors (SITBs) are increasing dramatically among children and adolescents. Crisis support is intended to provide immediate mental health care, risk mitigation, and intervention for those experiencing SITBs and acute mental health distress. Digital mental health interventions (DMHIs) have emerged as accessible and effective alternatives to in-person care; however, most do not provide crisis support or ongoing care for children and adolescents with SITBs. Objective To inform the development of digital crisis support and mental health care for children and adolescents presenting with SITBs, this study aims to (1) characterize children and adolescents with SITBs who participate in a digital crisis response service, (2) compare anxiety and depressive symptoms of children and adolescents presenting with SITBs versus those without SITBs throughout care, and (3) suggest future steps for the implementation of digital crisis support and mental health care for children and adolescents presenting with SITBs. Methods This retrospective study was conducted using data from children and adolescents (aged 1-17 y; N=2161) involved in a pediatric collaborative care DMHI. SITB prevalence was assessed during each live session. For children and adolescents who exhibited SITBs during live sessions, a rapid crisis support team provided evidence-based crisis support services. Assessments were completed approximately once a month to measure anxiety and depressive symptom severity. Demographics, mental health symptoms, and change in the mental health symptoms of children and adolescents presenting with SITBs (group with SITBs) were compared to those of children and adolescents with no SITBs (group without SITBs). Results Compared to the group without SITBs (1977/2161, 91.49%), the group with SITBs (184/2161, 8.51%) was mostly made up of adolescents (107/184, 58.2%) and female children and adolescents (118/184, 64.1%). At baseline, compared to the group without SITBs, the group with SITBs had more severe anxiety and depressive symptoms. From before to after mental health care with the DMHI, the 2 groups did not differ in the rate of children and adolescents with anxiety symptom improvement (group with SITBs: 54/70, 77% vs group without SITBs: 367/440, 83.4%; χ21=1.2; P=.32) as well as depressive symptom improvement (group with SITBs: 58/72, 81% vs group without SITBs: 255/313, 81.5%; χ21=0; P=.99). The 2 groups also did not differ in the amount of change in symptom severity during care with the DMHI for anxiety (t80.20=1.37; P=.28) and depressive (t83.75=–0.08; P=.99) symptoms. Conclusions This study demonstrates that participation in a collaborative care DMHI is associated with improved mental health outcomes in children and adolescents experiencing SITBs. These results provide preliminary insights for the use of pediatric DMHIs in crisis support and mental health care for children and adolescents presenting with SITBs, thereby addressing the public health issue of acute mental health crisis in children and adolescents.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.037
GPT teacher head0.483
Teacher spread0.446 · 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
Published2024
Admission routes1
Has abstractyes

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