Domestic violence and associated factors during COVID-19 epidemic: an online population-based study in Iran
Bibliographic record
Abstract
BACKGROUND: The novel coronavirus disease 2019 has severely affected communities around the world. Fear and stress of being infected, along with pressure caused by lockdown, prevention protocols, and the economic downturn, increased tension among people, which consequently led to the rise of domestic violence (DV). Therefore, this study was conducted to determine the rate of change in DV and its associated factors during the COVID-19 epidemic in Shiraz, Iran. METHODS: In this cross-sectional study, 653 individuals with the age of over 15 years from Shiraz were participated through snowball sampling and filled out an online questionnaire through the WhatsApp platform. A 51-item, self-administered and multidimensional (knowledge, attitude, and practice) questionnaire was designed and assessed 653 participants. The gathered data was analyzed using SPSS software (version 25), and variables with a p-value of less than 0.05 were considered statistically significant. RESULTS: In this study, 64.2% of the respondents were within the age range of 31-50 years, and 72.6% of the subjects were female. Furthermore, 73.8 and 73.0% of the individuals were married and educated for over 12 years, respectively. The DV increased by 37.5% during the quarantine period, compared to before the pandemic. The emotional type was the most common type of violence; the sexual type was the least frequent. Multivariate analysis indicated that infection with COVID-19, drug use, high level of co-living observation of anti-COVID prevention protocols, and lower level of physical activity during the quarantine period had a positive and significant association with the occurrence of DV. CONCLUSION: Based on the obtained results, it is required to implement effective harm-reduction policies and measures in the community due to the increasing rate of DV during the COVID-19 epidemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 teacher head, 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".