Sex/Gender Differences in the Cognitive‐Aging Effects of Head Injuries due to Interpersonal Violence
Bibliographic record
Abstract
Abstract Background Traumatic brain injury is a risk factor for worse later‐life brain health, including dementia. Yet the role of interpersonal violence and its gendered nature in the TBI–cognition relationship has yet to be fully studied. While men and women alike commit and experience violence, gender‐based violence (GBV)—which primarily targets women, transgender and gender‐nonconforming people, and from which they tend to suffer worse injuries than men—is understudied. Using population‐based survey data, we examine gender differences in cognitive effects of self‐reported head injuries due to interpersonal violence. Method We use data from the Health and Retirement Study (HRS; n = ), a survey of Americans age 51+. It gathered self‐reported lifetime head‐injury data in 2014, allowing participants to specify injury causes; we restricted our analysis to participants injured in a physical altercation or shot in the head. Participants also indicated injury severity in several binary variables. We calculated summary statistics to compare injury prevalence between men and women. To assess cognitive effects, we used multilevel regressions with subsequent cognitive performance (27 points possible) as the outcome variable and interaction between injury severity and sex/gender as the primary predictor variable, adjusting for race/ethnicity, education, and wealth. Result While men were likelier to report head injuries due to fights (c2 = 12.884; p < 0.001), women were over twice as likely to report ongoing health problems due to head injury (c2 = 10.638; p = 0.001), likelier to have been hospitalized due to a fight or gunshot‐caused head injury (c2 = 5.533; p = 0.019), and nearly 5x likelier to report losing consciousness due to choking (c2 = 5.069; p = 0.024). Women hospitalized due to a fight or gunshot experienced worse cognitive effects (b = ‐2.428; p = 0.004) than hospitalized men relative to unhospitalized men (b = ‐2.600, p = 0.005 and b = ‐3.372, p < 0.001, respectively). Conclusion In keeping with existing literature, women suffer worse injuries than men from interpersonal violence; women also have worse cognitive harms due to head injuries than do men. The reasons for these outcomes, and their relationship to GBV, requires further investigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".