The relationship between aggressive behaviors of preschool children and the violence against Iranian women in the COVID-19 pandemic
Bibliographic record
Abstract
BACKGROUND: During epidemics, supports are limited and individual and collective vulnerabilities as well as domestic violence are increased. Therefore, various groups in society, especially children and their mothers, are extremely vulnerable. This study aimed to assess the relationship between aggressive behaviors of preschool children and the violence against Iranian women during the COVID-19 pandemic. METHODS: This descriptive-correlational study was conducted in October-November 2020. Stratified random sampling was performed among preschool children in Kerman. Data were collected using the Violence toward Women Inventory and the Aggression scale for preschoolers Scale. Data were analyzed using SPSS25, ANOVA, independent t-test, and Pearson correlation test. RESULTS: The results showed that the total mean scores of violence against women and preschoolers' aggression were 54.43 ± 10.6 and 88.44 ± 6.5, respectively. The results showed a statistically significant difference in aggressive behaviors of preschool children, mother's job, number of children, mother's education, income, and age. A positive and significant relationship was also found between the subscales of violence against women and aggression in preschool children. CONCLUSIONS: The results showed a positive and significant relationship between violence against women and aggression of preschool children. Therefore, it is recommended that parents identify and eliminate the risk factors for domestic violence during the COVID-19 in order to protect their children. Parents also must learn coping strategies for stress and resilience in the epidemic crises.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".