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Record W2946506703 · doi:10.2196/12347

A Mobile Phone–Based Intervention to Improve Mental Health Among Homeless Young Adults: Pilot Feasibility Trial

2019· article· en· W2946506703 on OpenAlexvenueno aff
Stephen M. Schueller, Angela C Glover, Anne K. Rufa, Claire L. Dowdle, Gregory D. Gross, Niranjan S. Karnik, Alyson K. Zalta

Bibliographic record

VenueJMIR mhealth and uhealth · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Institute on Drug AbuseNational Institute of Mental HealthQuality Enhancement Research InitiativeNational Institutes of HealthRush UniversityWashington University in St. LouisHedge Funds CareU.S. Department of Veterans Affairs
KeywordsMental healthPsychosocialmHealthIntervention (counseling)Mobile phonePsychologyPhoneBrief interventionCoping (psychology)Randomized controlled trialPsychoeducationPsychological interventionMedicineClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Youth homelessness is a substantial issue, and many youths experiencing homelessness have mental health issues as both a cause and consequence of homelessness. These youths face many barriers to receiving traditional mental health services, and as a result, only a few youths experiencing homelessness receive any form of mental health care. OBJECTIVE: This project aimed to develop and determine the feasibility and acceptability of engaging young adults (ie, individuals aged 18-24 years) experiencing homelessness in a remotely delivered mental health intervention. This intervention provided brief emotional support and coping skills, drawing from cognitive behavioral principles as an introduction into psychosocial support. The intervention was piloted in a homeless shelter network. METHODS: A total of 35 young adults experiencing homelessness participated in a single-arm feasibility pilot trial. Participants received a mobile phone, a service and data plan, and 1 month of support from a coach consisting of up to 3 brief phone sessions, text messaging, and mobile mental health apps. We evaluated feasibility by looking at completion of sessions as well as the overall program and acceptability with satisfaction ratings. We also collected clinical symptoms at baseline and the end of the 1-month support period. We used validity items to identify participants who might be responding inappropriately and thus only report satisfaction ratings and clinical outcomes from valid responses. RESULTS: Most participants (20/35, 57%) completed all 3 of their phone sessions, with an average of 2.09 sessions (SD 1.22) completed by each participant. Participants sent an average of 15.06 text messages (SD 12.62) and received an average of 19.34 messages (SD 12.70). We found higher rates of satisfaction among the participants with valid responses, with 100% (23/23) of such participants indicating that they would recommend participation to someone else and 52% (12/23) reporting that they were very or extremely satisfied with their participation. We found very little change from pre- to posttreatment on measures of depression (d=0.27), post-traumatic stress disorder (d=0.17), and emotion regulation (d=0.10). CONCLUSIONS: This study demonstrated that it was feasible to engage homeless young adults in mental health services in this technology-based intervention with high rates of satisfaction. We did not find changes in clinical outcomes; however, we had a small sample size and a brief intervention. Technology might be an important avenue to reach young adults experiencing homelessness, but additional work could explore proper interventions to deliver with such a platform. TRIAL REGISTRATION: ClinicalTrials.gov NCT03620682; https://clinicaltrials.gov/ct2/show/NCT03620682.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.057
GPT teacher head0.450
Teacher spread0.393 · 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 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

Citations80
Published2019
Admission routes1
Has abstractyes

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