Sexist attitudes predict family-based aggression during a COVID-19 lockdown.
Bibliographic record
Abstract
The current research examined whether men's hostile sexism was a risk factor for family-based aggression during a nationwide COVID-19 lockdown in which families were confined to the home for 5 weeks. Parents who had reported on their sexist attitudes and aggressive behavior toward intimate partners and children prior to the COVID-19 pandemic completed assessments of aggressive behavior toward their partners and children during the lockdown (N = 362 parents of which 310 were drawn from the same family). Accounting for pre-lockdown levels of aggression, men who more strongly endorsed hostile sexism reported greater aggressive behavior toward their intimate partners and their children during the lockdown. The contextual factors that help explain these longitudinal associations differed across targets of family-based aggression. Men's hostile sexism predicted greater aggression toward intimate partners when men experienced low power during couples' interactions, whereas men's hostile sexism predicted greater aggressive parenting when men reported lower partner-child relationship quality. Novel effects also emerged for benevolent sexism. Men's higher benevolent sexism predicted lower aggressive parenting, and women's higher benevolent sexism predicted greater aggressive behavior toward partners, irrespective of power and relationship quality. The current study provides the first longitudinal demonstration that men's hostile sexism predicts residual changes in aggression toward both intimate partners and children. Such aggressive behavior will intensify the health, well-being, and developmental costs of the pandemic, highlighting the importance of targeting power-related gender role beliefs when screening for aggression risk and delivering therapeutic and education interventions as families face the unprecedented challenges of COVID-19. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".