Examining the profile of couples attending a violence prevention intervention: A dyadic latent profile analysis
Bibliographic record
Abstract
Objective: Existing research points to how increasing male engagement in public health interventions without addressing gender inequities can perpetuate power imbalances in a relationship which may lead to increased stress and anxiety for women. The aim of this study was to identify profiles of couples who attended a violence prevention intervention based on their attitudes toward gender norms and power sharing within a couple, determine if these profiles have different women's mental health outcome and examine whether male engagement moderates this relationship. Methods: Using data from a cluster randomized controlled trial conducted in North Kivu, Democratic Republic of Congo, we conducted a latent profile analysis to categorize different profiles amongst couples in the intervention (n=202) and conduct logistic regression to examine the study aims. Results: The latent profile analysis identified three profiles of couples: (1) discordant dyads, (2) concordant high gender equity dyads, and (3) concordant average dyads. There were no differences in women's depression symptoms at the end of the intervention amongst the three profiles. Male engagement, measured through attendance data, did not moderate the relationship between the couple's relationship profile and women's mental health. Conclusion: Small sample size, measurement sensitivity, and potential response bias to the scales assessing gender norms, power dynamics and mental well-being might have led to the null results we see. Yet, future studies should further explore the potential for differences in mental health outcomes and the impact of interventions on these outcomes based on intercouple dynamics in understanding and expression of power and gender norms.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".