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Record W2906229705 · doi:10.2196/11591

An Online Mindfulness-Based Cognitive Behavioral Therapy Intervention for Youth Diagnosed With Major Depressive Disorders: Protocol for a Randomized Controlled Trial

2018· article· en· W2906229705 on OpenAlexaffvenueabout
Paul Ritvo, Zafiris J. Daskalakis, George Tomlinson, Arun Ravindran, Renee Linklater, Megan Kirk, Yuliya Knyahnytska, Jonathan Lee, Nazanin Alavi, Shari Bai, Lillian Harber, Tania Jain, Joel Katz

Bibliographic record

VenueJMIR Research Protocols · 2018
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity Health NetworkCentre for Addiction and Mental HealthUniversity of TorontoYork University
Fundersnot available
KeywordsRandomized controlled trialPsychiatryMindfulnessMental healthSuicidal ideationClinical psychologyMajor depressive disorderMedicineEthnic groupPsychologyCognitionPoison controlSuicide prevention

Abstract

fetched live from OpenAlex

Background About 70% of all mental health disorders appear before the age of 25 years. When untreated, these disorders can become long-standing and impair multiple life domains. When compared with all Canadian youth (of different ages), individuals aged between 15 and 25 years are significantly more likely to experience mental health disorders, substance dependencies, and risks for suicidal ideation and death by suicide. Progress in the treatment of youth, capitalizing on their online responsivity, can strategically address depressive disorders. Objective We will conduct a randomized controlled trial to compare online mindfulness-oriented cognitive behavioral therapy (CBT-M) combined with standard psychiatric care versus psychiatric care alone in youth diagnosed with major depressive disorder. We will enroll 168 subjects in the age range of 18 to 30 years; 50% of subjects will be from First Nations (FN) backgrounds, whereas the other 50% will be from all other ethnic backgrounds. There will be equal stratification into 2 intervention groups (INT1 and INT2) and 2 wait-list control groups (CTL1 and CTL2) with 42 subjects per group, resulting in an equal number of INT1 and CTL1 of FN background and INT2 and CTL2 of non-FN background. Methods The inclusion criteria are: (1) age 18 to 30 years, FN background or other ethnicity; (2) Beck Depression Inventory (BDI)-II of at least mild severity (BDI-II score ≥14) and no upper limit; (3) Mini-International Neuropsychiatric Interview (MINI)–confirmed psychiatric diagnosis of major depressive disorder; and (4) fluent in English. All patients are diagnosed by a Centre for Addiction and Mental Health psychiatrist, with diagnoses confirmed using the MINI interview. The exclusion criteria are: (1) individuals receiving weekly structured psychotherapy; (2) individuals who meet the Diagnostic and Statistical Manual of Mental Disorders criteria for severe alcohol/substance use disorder in the past 3 months, or who demonstrate clinically significant suicidal ideation defined as imminent intent, or who have attempted suicide in the past 6 months; and (3) individuals with comorbid diagnoses of borderline personality, schizophrenia, bipolar disorder, and/or obsessive compulsive disorder. All subjects are provided standard psychiatric care defined as 1 monthly session that focuses on appropriate medication, with session durations of 15 to 30 min. Experimental subjects receive an additional intervention consisting of the CBT-M online software program (in collaboration with Nex J Health, Inc). Exposure to and interaction with the online workbooks are combined with navigation-coaching delivered by phone and secure text message interactions. Results The outcomes selected, combined with measurement blinding, are key features in assessing whether significant benefits regarding depression and anxiety symptoms occur. Conclusions If results confirm the hypothesis that youth can be effectively treated with online CBT-M, effective services may be widely delivered with less geographic restriction. International Registered Report Identifier (IRRID) PRR1-10.2196/11591

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.243
GPT teacher head0.575
Teacher spread0.332 · 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 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

Citations10
Published2018
Admission routes3
Has abstractyes

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