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Record W2932927579 · doi:10.2196/10904

Text Messaging to Enhance Behavioral Health Treatment Engagement Among Justice-Involved Youth: Qualitative and User Testing Study

2019· article· en· W2932927579 on OpenAlexvenueno aff
Marina Tolou‐Shams, Juliet Yonek, Eraka Bath

Bibliographic record

VenueJMIR mhealth and uhealth · 2019
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Advancing Translational SciencesFeinberg School of MedicineNorthwestern University
KeywordsmHealthRecidivismReceiptMental healthPsychological interventionEconomic JusticeShort Message ServicePsychologyPopulationPsychiatryClinical psychologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Mental health and substance use disorders are highly prevalent in justice-involved youth, yet only 8% of court-involved, nonincarcerated (CINI) youth in need of treatment receive it. Dual diagnosis (co-occurring psychiatric and substance use disorders) in justice-involved youth is highly predictive of recidivism. Identifying novel approaches, such as the use of mobile health (mHealth) technologies, to close this gap between need and receipt of behavioral health treatment for the CINI population could potentially offset rates of reoffending into adulthood. Text-messaging (short message service, SMS) interventions have demonstrated efficacy in improving treatment adherence and other associated outcomes in other vulnerable youth populations, but development and testing of mHealth interventions to improve behavioral health treatment rates and outcomes for CINI youth are lacking. OBJECTIVE: This study aimed to collect qualitative data from key stakeholders to inform the development of a theoretically grounded, family-based text-messaging (SMS) intervention targeting CINI youth's behavioral health treatment engagement; additionally, the aim was to conduct end-user testing over 6 months with CINI youth and caregivers to determine intervention feasibility and acceptability. METHODS: CINI youth and caregivers were referred from a California-based Juvenile Probation Department and community-based provider organizations providing services for justice-involved youth. Eligibility criteria included the following: being a justice-involved youth or a caregiver of a justice-involved youth, English speaking, youth aged 13 to 17 years old and either referred to or currently attending mental health or substance use treatment, and youth and caregiver have access to a cell phone with text-messaging capability. RESULTS: Overall, 28 individuals participated in focus groups and interviews-8 youth, 5 caregivers, and 15 juvenile justice (JJ) personnel. Three major themes emerged: (1) texting among JJ personnel and CINI youth and caregivers in their caseload is common but not systematic, (2) stigma and privacy are perceived as barriers to texting youth about behavioral health treatment appointments, and (3) messages should be short, simple, relatable, positive, and personalized. In total, 9 participants (7 youth and 2 caregivers) participated in end-user testing and rated the intervention as useful, helpful, and supportive. CONCLUSIONS: Text messaging (SMS) is an acceptable and feasible means of reminding CINI youth to attend behavioral health treatment appointments. Future implementation challenges include making text messaging (SMS) personalized and tailored but not resource intensive (eg, requiring one-to-one, 24/7 human contact) and identifying which systems will deliver and sustain the intervention. Text messaging (SMS) among justice personnel, youth, and their caregivers is already widespread, but lack of clear guidelines about privacy, confidentiality, and information sharing poses ethical conundrums. Future hybrid-type research designs that explore the efficacy of the intervention while also studying ethical, system, and policy-level factors associated with using digital health interventions to improve CINI youth outcomes is a key next step.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.249
GPT teacher head0.549
Teacher spread0.300 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations35
Published2019
Admission routes1
Has abstractyes

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