Familial Risk and Dowry Demand: Are they Causal Factors for Physical and Psychological Violence among Women? A Structural Equation Modeling
Bibliographic record
Abstract
Background: In India, studies dealt with domestic violence have used linear or logistic regression to present risk factors. These methods do not allow studying the impact of intermediate variables on the path which could exert indirect or mediation effects on the outcome. This study investigated the direct and indirect effects of familial risk and dowry demand on physical and psychological violence through the mediating variables: alcohol use, women characteristics and social support. Study design: A population-based, cross-sectional household survey was conducted at seven sites in six states across India, based on 9938 women. Methods: Confirmatory Factor Analysis and Structural equation models were used to investigate the associations of familial risk, dowry demand and mediating variable use with physical and psychological violence. Models were assessed using goodness of fit statistics. Results: The direct and indirect relationship between familial risk and physical violence with regression coefficient was 0.323 and 0.100 respectively. Similarly, for psychological violence was 0.151 and 0.371 respectively. The dowry demand had indirect effect (0.209) on psychological violence through the mediating variables such as alcohol use, women characteristics, social support and physical violence as compared to direct effects (0.112). The model fit statistics had a moderately good fit with RMSEA=0.09, Chi square with p<0.001 and CFI 0.87. Conclusion: Despite the fact that the women were exposed to abuse during childhood period the mediating variables such as social support, women characteristics and Husbands alcohol use etc., have a significant role to play to contain the both physical and psychological violence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".