The impact of providing personalized depression risk information on self‐help and help‐seeking behaviors: Results from a mixed methods randomized controlled trial
Bibliographic record
Abstract
OBJECTIVES: To evaluate the impact of providing personalized depression risk information on self-help and help-seeking behaviors among individuals who are at high risk of having a major depressive episode (MDE). MATERIALS AND METHODS: In a mixed methods randomized controlled trial, participants who were at high risk of having a MDE, were recruited from across Canada, and were randomized into intervention (n = 358) and control (n = 354) groups. Participants in the intervention group received their personalized depression risk estimated by sex-specific risk prediction models for MDE. All participants were assessed at baseline, 6 and 12 months. RESULTS: Repeated measure mixed effects modeling showed significant between group differences in self-help scores. In the complete case analysis, the between group difference in mean self-help change score was 1.13 at 12 months (effect size = 0.16). Among participants who reported "fair" or "poor health," the between group difference in mean self-help change score was 2.78 at 12 months (effect size = 0.35). The qualitative data revealed three themes and the findings are consistent with the quantitative results. CONCLUSIONS: Providing personalized depression risk information has a positive impact on self-help in high-risk individuals, particularly in those with poor health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".