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Record W4393940890 · doi:10.1186/s13063-024-08076-y

Increasing access to mental health supports for 12–17-year-old Indigenous youth with the JoyPop mobile mental health app: study protocol for a randomized controlled trial

2024· article· en· W4393940890 on OpenAlexafffundabout
Aislin R. Mushquash, Teagan Neufeld, Ishaq Malik, Elaine Toombs, Janine V. Olthuis, Fred Schmidt, C. Dunning, Kristine Stasiuk, Tina Bobinski, Arto Öhinmaa, Amanda S. Newton, Sherry H. Stewart

Bibliographic record

VenueTrials · 2024
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of AlbertaDalhousie UniversityFirst Nations Health and Social Secretariat of ManitobaUniversity of New BrunswickThunder Bay Regional Health Sciences CentreLakehead University
FundersCanadian Institutes of Health ResearchSick Kids Foundation
KeywordsMental healthRandomized controlled trialMedicineIndigenousIntervention (counseling)DistressPsychiatryClinical psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Indigenous youth in Northwestern Ontario who need mental health supports experience longer waits than non-Indigenous youth within the region and when compared to youth in urban areas. Limited access and extended waits can exacerbate symptoms, prolong distress, and increase risk for adverse outcomes. Innovative approaches are urgently needed to provide support for Indigenous youth in Northwestern Ontario. Using a randomized controlled trial design, the primary objective of this study is to determine the effectiveness of the JoyPop app compared to usual practice (UP; monitoring) in improving emotion regulation among Indigenous youth (12-17 years) who are awaiting mental health services. The secondary objectives are to (1) assess change in mental health difficulties and treatment readiness between youth in each condition to better understand the app's broader impact as a waitlist tool and (2) conduct an economic analysis to determine whether receiving the app while waiting for mental health services reduces other health service use and associated costs. METHODS: A pragmatic, parallel arm randomized controlled superiority trial will be used. Participants will be randomly allocated in a 1:1 ratio to the control (UP) or intervention (UP + JoyPop) condition. Stratified block randomization will be used to randomly assign participants to each condition. All participants will be monitored through existing waitlist practices, which involve regular phone calls to check in and assess functioning. Participants in the intervention condition will receive access to the JoyPop app for 4 weeks and will be asked to use it at least twice daily. All participants will be asked to complete outcome measures at baseline, after 2 weeks, and after 4 weeks. DISCUSSION: This trial will evaluate the effectiveness of the JoyPop app as a tool to support Indigenous youth waiting for mental health services. Should findings show that using the JoyPop app is beneficial, there may be support from partners and other organizations to integrate it into usual care pathways. TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT05898516 [registered on June 1, 2023].

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Randomized trialhigh
gptno category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Randomized trialhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.029
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.098
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.154
GPT teacher head0.532
Teacher spread0.377 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreProtocol

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

Citations4
Published2024
Admission routes3
Has abstractyes

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