Youth living in Roma communities and their beliefs related to intimate partner violence
Bibliographic record
Abstract
Background Intimate partner violence attracts the attention of public health professionals who are looking to explore its predictors such as economic situation and life satisfaction. Particularly, the focus is on young people who represent the target population for community interventions so this study examined to what extent judgmental beliefs and attitudes related to intimate partner violence are present among young women and men (15-24 years old) living in Serbian Roma settlements. Methods Data came from the 2010 Multiple Indicator Cluster Survey conducted in Serbia. Associations of judgmental attitudes with a wide range of socio-demographic factors and life satisfaction were tested using univariate and multivariate logistic regression analyses. Results One third of men and one quarter of women believed that under certain circumstances men are justified to be violent towards wives. In univariate models, in both men and women, judgmental believes were associated with lower educational level, lower socio-economic status and being married, while satisfaction with friendship among women and satisfaction with school among men had protective effects. In multivariate models, among women all factors remained to be significant predictors of judgmental believes (education: primary OR 0.51, 95%CI 0.32-0.79, secondary OR 0.15, 95%CI 0.06 -0.38, compared to no education ; marital status: formerly married OR 0.38, 95%CI 0.18-0.79, never married OR 0.56, 95%CI 0.36-0.95, compared to currently married; wealth index: poor OR 0.57, 95%CI 0.35-0.93, rich OR 0.51, 95%CI 0.28-0.91, compared to poorest). For men, only the wealth index was significant (richest: OR 0.40, 95%CI 0.18-0.87, compared to poorest). Conclusions Violence prevention activities have to be focused on promoting gender equality in vulnerable population groups such as Roma, especially through strengthening their education and employment. Key messages: Social development programs that are focused on keeping youth within schools as longer as possible, and teaching them positive gender norms and values, should be priority for action The results should be in the focus of primary violence prevention campaigns, whose benefits would certainly exceed investments, with long-term positive effects on the well-being of the society
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".