Anxiety and prior victimization predict online gender-based violence perpetration among Indonesian young adults during COVID-19 pandemic: cross-sectional study
Bibliographic record
Abstract
Background: Most of human interactions moved to the cyberspace for much of the pandemic. It was no surprise that online violence was also on the rise. One of the objectives of this study was to describe the prevalence and risk factors of online gender-based violence (OGBV) perpetration during the COVID-19 pandemic. Results: The final analysis included 1006 respondents, 84.2% of whom were women and 94.5% were heterosexual. Over 60% of respondents admitted having perpetrated at least one type of OGBV once. It included 58.6% of women who admitted having perpetrated OGBV. Logistic regression analysis identified anxiety, online disinhibition, and history of victimization as independent risk factors of perpetration with an adjusted odds ratio (aOR) of 1.82 (95% CI 1.30-2.56), 1.38 (95% CI 1.03-1.85), and 9.72 (95% CI 5.11-18.51), respectively. Sub-group analysis that identified these factors also facilitated increased frequency and severity of OGBV perpetration. Conclusions: We found a high proportion of OGBV perpetration among young adults during the pandemic among all genders although women were grossly overrepresented among the respondents. Risk factors of perpetration included anxiety, online disinhibition, and prior victimization. The pandemic situation which heightened general anxiety and increased dependency on online communication may facilitate the perpetration of OGBV. The generalization of this result should pay attention to the caveat that the demographic of respondents is heavily skewed toward women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".