A smartphone-online intervention for youth diagnosed with major depressive disorders: Protocol for a randomized controlled trial. (Preprint)
Bibliographic record
Abstract
BACKGROUND Seventy percent of mental health problems appear before the age of 25 years and when untreated can become long-standing, and significant, impairing multiple life domains (1). Although the problem is especially acute for youth from First Nations backgrounds, all Canadian youth aged 15- to 25 years are highly likely to experience mental health disorders, substance dependencies and suicide. Progress in the treatment of youth that capitalizes on tendencies to respond to online contacts strategically addresses mental health problems, particularly depressive disorders. OBJECTIVE We will conduct a randomized controlled trial (RCT), to compare online mindfulness-based cognitive behavioural therapy combined with standard psychiatric care vs. psychiatric care alone (wait-list controls) in youth diagnosed with major depressive disorder. We will enrol N = 168 subjects in the age range of 18-30 years, 50% of whom will be from First Nations backgrounds and the other 50% from all other ethnic backgrounds, equally stratified in two intervention groups and two (wait-list) control groups (42 subjects per group, where INT1 and CTL1 are FN background, and INT2 and CTL2 are non-FN background). METHODS In this RCT, the primary outcome will be self-reported depression on the Beck Depression Inventory II. Secondary outcomes include anxiety (Beck Anxiety Inventory), depression (Quick Inventory of Depressive Symptomatology, 24-item Hamilton Rating Scale for Depression (HRSD-24)), pain (Brief Pain Inventory) and mindfulness (Five-Facet Mindfulness Questionnaire). RESULTS Recruitment/retention rates will be assessed with estimates for the proportion of participants with complete data per outcome and time points divided by the total number of study participants. Variability of the main and interaction effects will be examined in the primary clinical outcome and each secondary outcome using separate repeated measures ANCOVA models, with Bonferroni corrections applied to the models applied. Hedges' g and associated confidence intervals will be calculated as an estimate of the effect size both over time (within groups) and between groups. Missing data will be evaluated on a case-by-case basis such that drop-outs will be excluded. CONCLUSIONS If results confirm hypotheses that youth can be effectively treated with online mindfulness-based cognitive behavioural therapy at reduced costs, effective services can be delivered more widely with less geographic restrictions. CLINICALTRIAL Clinical Trials.gov
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.124 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".