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Record W4283458732 · doi:10.2196/35685

Feasibility of Text Messages for Enhancing Therapeutic Engagement Among Youth and Caregivers Initiating Outpatient Mental Health Treatment: Mixed Methods Study

2022· article· en· W4283458732 on OpenAlexaffvenue
Susan Jerrott, Sharon Clark, Jill Chorney, Aimée Coulombe, Lori Wozney

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsDalhousie UniversityIzaak Walton Killam Health CentreNova Scotia Health Authority
Fundersnot available
KeywordsMental healthPsychological interventionMultidisciplinary approachIntervention (counseling)MedicineAnalyticsPsychologyNursingPsychiatryComputer scienceData science

Abstract

fetched live from OpenAlex

BACKGROUND: Pathways to mental health services for youth are generally complex and often involve numerous contact points and lengthy delays. When starting treatment, there are a host of barriers that contribute to low rates of therapeutic engagement. Automated text messages offer a convenient, low-cost option for information sharing and skill building, and they can potentially activate positive behaviors in youth and caregivers prior to beginning formal therapy. To date, there is little evidence for the feasibility of initiating transdiagnostic text messages during the early stages of youth and caregiver contact with community outpatient mental health services. OBJECTIVE: To develop and test the feasibility of implementing 2 novel text messaging campaigns aimed at youth clients and their caregivers during the early stages of engaging with outpatient mental health services. METHODS: A multidisciplinary panel of experts developed two 12-message interventions with youth and caregivers prior to deployment. Each message included a link to an external interactive or multimedia resource to extend skill development. Enrollment of youth aged 13 to 18 years, their caregivers, or both occurred at 2 early treatment timepoints. At both time points, text messages were delivered automatically 2 times a week for 6 weeks. Analytics and survey data were collected in 2 phases, between January and March 2020 and between January and May 2021. Enrollment, willingness to persist in using the intervention, engagement, satisfaction, perceived value, and impact were measured. Descriptive statistics were used to summarize youth and caregiver outcomes. RESULTS: A total of 41 caregivers and 36 youth consented to participate. Follow-up survey response rates were 54% (22/41) and 44%, (16/36) respectively. Over 1500 text messages were sent throughout the study. More than three-quarters (14/16, 88%) of youth reported that they learned something new and noticed a change in themselves due to receiving the texts; the same proportion (14/16, 88%) of youth said they would recommend the text messages to others. Youth ranked the first text message, related to coping with difficult emotions, as the most helpful of the series. Caregivers reported acting differently due to receiving the texts. Over two-thirds of caregivers were satisfied with the texts (16/22, 73%) and would recommend them to others (16/22, 73%). Caregivers perceived diverse levels of value in the text topics, with 9 of the 12 caregiver texts rated by at least one caregiver as the most helpful. CONCLUSIONS: Results are preliminary but show that brief, core skill-focused text messages for youth clients and caregivers in community outpatient mental health services are feasible. Both youth and caregivers reported promising knowledge and behavior change with exposure to only 12 messages over 6 weeks. A larger study with statistical power to detect changes in both perceived helpfulness and engagement is required to confirm the effectiveness of this type of transdiagnostic intervention.

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)0.029
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.154

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.293
GPT teacher head0.576
Teacher spread0.284 · 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 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

Citations6
Published2022
Admission routes2
Has abstractyes

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