Temporal pathways of change in two randomized controlled trials for depression and harmful drinking in Goa, India
Bibliographic record
Abstract
BACKGROUND: The current study explored the temporal pathways of change within two treatments, the Healthy Activity Program (HAP) for depression and the Counselling for Alcohol Problems (CAP) Program for harmful drinking. METHODS: The study took place in the context of two parallel randomized controlled trials in Goa, India. N = 50 random participants who met a priori criteria were selected from each treatment trial and examined for potential direct and mediational pathways. In HAP, we examined the predictive roles of therapy quality and patient-reported activation, assessing whether activation mediated the effects of therapy quality on depression (Patient Health Questionnaire-9) outcomes. In CAP, we examined the predictive roles of therapy quality and patient change- and counter-change-talk, assessing whether change- or counter-change-talk mediated the effects of therapy quality on daily alcohol consumption. RESULTS: In HAP, therapy quality (both general and treatment-specific skills) was associated with patient activation; patient activation but not therapy quality significantly predicted depression outcomes, and patient activation mediated the effects of higher general skills on subsequent clinical outcomes [a × b = -2.555, 95% confidence interval (CI) -5.811 to -0.142]. In CAP, higher treatment-specific skills, but not general skills, were directly associated with drinking outcomes, and reduced levels of counter-change talk both independently predicted, and mediated the effects of higher general skills on, reduced alcohol consumption (a × b = -24.515, 95% CI -41.190 to -11.060). Change talk did not predict alcohol consumption and was not correlated with counter-change talk. CONCLUSION: These findings suggest that therapy quality in early sessions operated through increased patient activation and reduced counter-change talk to reduce depression and harmful drinking respectively.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| 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".