Psychological treatments for depression among women experiencing intimate partner violence: findings from a randomized controlled trial for behavioral activation in Goa, India
Bibliographic record
Abstract
Intimate partner violence (IPV) strongly predicts depression, but it is unknown if women experiencing IPV can benefit from depression treatments in contexts where depression and IPV are prevalent. This study explored whether women experiencing IPV in Goa, India, can benefit from the Healthy Activity Program (HAP), a culturally adapted behavioral activation treatment, compared with enhanced usual care (EUC). Cross-sectional and longitudinal analyses were performed on data from a clinical trial. Measures assessed at baseline and 3 and 12 months included depressive symptoms. Measures assessed at 3 and 12 months included activation and IPV. Independent t tests were conducted to assess if participants experiencing IPV had higher depressive symptoms and lower activation at 3 and 12 months; hierarchical linear regression was conducted to determine if 3-month IPV predicted 12-month depressive symptoms across trial arms (Hypothesis 1). Hierarchical linear regression was then conducted to examine if the relationship between 3-month activation and 12-month depressive symptoms was moderated by 3-month IPV within each trial arm (Hypothesis 2). As expected, participants experiencing IPV had significantly lower activation levels and higher depressive symptoms compared with participants who did not experience IPV at 3 and 12 months in cross-sectional analyses. Similarly, IPV endorsed at 3 months significantly predicted depressive symptoms at 12 months. However, activation was significantly associated with less severe depressive symptoms at 12 months, irrespective of IPV endorsement among HAP participants. For EUC participants, IPV remained the only significant predictor of depressive symptoms at 12 months. Results suggest that women experiencing IPV can still benefit from behavioral activation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".