A Survey of the Effect of Risk Perception and Socioeconomic Status on Coping Strategies in Violent Situations Against Women
Bibliographic record
Abstract
Objective: Violence against women is a severe mental health problem. Much research has been done separately on the perception of risk, socioeconomic status (SES), and coping strategies of women who are victims of domestic violence. This study aims to investigate how women's risk perception and socioeconomic status affect their choice of coping strategies and to compare women who are victims of violence with women who have not experienced domestic violence. Method: The statistical population is married women in Iran, 312 women were selected as a sample through random sampling. To measure the variables, four questionnaires were used: Ghodratnama's Socioeconomic Status (SES), Haj-Yahia's Questionnaire of Violence Against Women, Benthin Risk Perception Scale, and Jalowiec Coping Scale. After the data collection stage, the relationship between the variables was analyzed using SPSS software and Pearson's correlation and linear regression. Results: In women victims of violence, risk perception was significantly related to fatalistic and palliative strategies, and SES was significantly related to evasive strategy (p<0.05). In women who were not subjected to domestic violence, risk perception had a significant relationship with optimistic, fatalistic, and emotive strategies (p<0.05). The correlation coefficient between risk perception and fatalistic and emotive strategy was very weak and can be ignored. Conclusion: The present study showed that the perception of risk affects the fatalistic and palliative coping strategies of women who are victims of violence, and their low socioeconomic status leads to more use of passive strategies such as evasive. And women who have not experienced domestic violence, the less they see themselves in danger of violence, the more they will use optimistic strategies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".