Correlation between Religious Attitude and Resiliency of Women under Domestic Violence
Bibliographic record
Abstract
The most common type of violence against women is domestic violence so that it is considered as a general health crisis. Resilience is a self-healing method featured with positive emotional outcomes. Religious attitudes are of the probable grounds of development of resilience techniques among women who have been victim of domestic violence. The present study is aimed at determining correlation between religious attitudes and resilience among women with domestic violence. The present study is a descriptive correlative research. Selected Health centers in east and north districts of Tehran affiliated with Shahid Beheshti University of Medical Sciences. Data gathering tools were a demographic information questionnaire, Connor-Davidson’s Resilience scale, and Serajzadeh’s “Muslim Religiosity questionnaire”. We used Pearson correlation test to indicate the presence of correlation between resiliency and religious attitudes. Data analyses were performed in SPSS 16. Mean and standard deviation of religious attitudes and resilience among the women with domestic violence were 76.41±9.96, 68.46±15.68 respectively. The results showed that there was correlation between religious attitude and resilience among women who have been subject to domestic violence. (P<0.001, r=0.24). Women under domestic violence had stronger religions attitudes and higher resilience strength. It appears that resilience among women subject to domestic violence and the effective factors on resilience and religious attitudes in particular (given the role of religion in Iranians’ lives) deserve special attention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".