Adaptation and outcomes of a lay-guided mental health self-care model: Results of six trials
Bibliographic record
Abstract
OBJECTIVE: To synthesize results of six controlled trials of self-care interventions for depression and/or anxiety, focusing on five trials in which lay guidance was compared to self-directed use of the same self-care tools. METHODS: The trials were conducted in Canada in different target populations. Self-care tools were adapted to each population. Guidance was provided in 3-15 calls over a period of 6-26 weeks. Depression and/or anxiety were assessed at follow-up (6-26 weeks). Pooled analyses used a meta-analytic approach. Engagement with the self-care tools was compared using the standardized difference or Cohen's d effect size. RESULTS: In studies with homogeneous outcomes (three for depression, four for anxiety), the pooled effect sizes of guidance vs. self-directed use of the self-care tools were 0.36 (95% CI 0.10, 0.62, N = 235) for depression and 0.21 (95% CI -0.03, 0.44, N = 285) for anxiety. Guidance consistently led to greater engagement with the tools. CONCLUSIONS: The intervention model is a potentially sustainable and accessible alternative to professionally guided self-care for people with mild-moderate depression. Factors which may have limited implementation success include: co-interventions, reduced number of guide calls (3 vs 6 or more), and delivery to dyads (patient-caregiver).
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".