Intimate partner violence and symptoms of psychological distress and depression in adolescents and young adults in Haiti
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is prevalent in low and middle-income countries, such as Haiti. However, there is little research on its association with mental health problems such as psychological distress and depression. Although there is evidence that men may experience IPV, few studies have investigated mental health difficulties among Haitian men and women. The present study aims to 1) assess the prevalence of depressive symptoms and psychological distress in this population and 2) examine the association between IPV, psychological distress, and depression while considering potential risk and protective factors. METHOD: A representative sample of 3,586 adolescents and young adults aged 15 to 24 living in Haiti was recruited. Structural equation modeling was used to examine the association between IPV, depressive symptoms, and psychological distress. RESULTS: Almost half of the sample reported depressive symptoms and psychological distress, with high rates among both genders. IPV was found to be an independent predictor of both depressive symptoms and psychological distress after accounting for risk and protective factors. LIMITATION: This study is the first step in understanding the interplay between IPV victimization, risk and protective factors, and psychological difficulties in this population. However, because of the cross-sectional design, causality should not be inferred. Furthermore, this study did not measure community violence, which could have affected participants' mental health. CONCLUSION: This study highlights the importance of considering the occurrence of IPV victimization when evaluating depression and psychological distress among adolescents and young adults.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".