Baseline Psychological Treatment Reduces the Effect of Coaching in a Randomised Trial of a Depression Self-Care Intervention
Bibliographic record
Abstract
OBJECTIVE: To explore the effects of baseline psychological and antidepressant medication treatment in a trial of lay telephone coaching in a low-intensity, supported depression self-care intervention. METHOD: A single blind, individually randomised, pragmatic trial was conducted among primary care adults with chronic physical conditions and comorbid depressive symptoms. Eligible subjects were randomised to receive a depression self-care toolkit with (intervention group) or without (control group) telephone coaching provided by trained lay coaches. For this brief communication, a secondary analysis of the trial data focused on the effects of baseline psychological and antidepressant treatments on mental health outcomes (Patient Health Questionnaire 9 [PHQ-9], SF-12 Mental Component Summary [MCS], Generalized Anxiety Disorder 7 [GAD-7]) and satisfaction with the intervention. RESULTS: In total, 223 patients were randomised, and 165 (74.0%) completed both 3- and 6-month follow-ups. There were 2 significant interactions of baseline treatment and study group for 6-month mental health outcomes. A significant benefit of coaching on 6-month PHQ-9 was seen only among participants who were not receiving baseline psychological treatment. A smaller interaction was found for baseline antidepressant medications and 6-month mental health. There was a significant main effect for baseline psychological treatment and lower 6-month satisfaction. CONCLUSIONS: Depressed patients receiving baseline psychological treatment may not benefit from lay coaching offered as part of a low-intensity depression self-care intervention.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".