Marital rape and its impact on the mental health of women in India: A systematic review
Bibliographic record
Abstract
This systematic review aims to describe the prevalence of marital rape in India, the analytic methods employed in its study, and its implications on mental health of victims. Online databases, PubMed, Embase, Web of Science and APA Psych, were systematically searched for articles published up until November 2020. Selected articles included those published from or studies conducted in India where the primary exposure was marital rape. The primary outcomes of interest are Post Traumatic Stress Disorder (PTSD) and Depression. Secondary outcomes related to PTSD and depression (e.g., suicidality) included in identified studies were also described. 11 studies were included after excluding studies based on our selection criteria: 9 quantitative studies and 2 qualitative studies. Sexual coercion by intimate partner was highly prevalent, ranging from 9%-80% and marital rape ranged from 2%-56%. Many of the studies reported statistically significant associations between marital rape and mental health outcomes, including clinical depression (7 of 8); PTSD (1 of 3). Quantitative studies were assessed for quality and risk of bias using the NIH Quality Assessment Scale and the modified Newcastle Ottawa Scale for cross-sectional and observational cohort studies, and most exhibited a low risk of bias. Qualitative studies identified a broad range of exposures and psychological sequlae of marital rape not captured by quantitative studies. Included publications exhibit a low to moderate association between marital rape and adverse mental health outcomes. Qualitative data also supplements these findings and provide relevant context. Further research on marital rape, its prevalence and consequences, is needed to advance policy, and health infrastructure on the subject.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".