Battered and Brain Injured: Traumatic Brain Injury Among Women Survivors of Intimate Partner Violence—A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: The objective of this scoping review is to examine the extent, range, and nature of literature targeting health-care professionals on the prevalence and outcome of intimate partner violence (IPV)-related traumatic brain injury (TBI). The purpose is to gain an understanding of prevalence, investigate screening tool use, generate IPV/TBI-specific support recommendations, and identify suggestions for future research. METHOD: The review was guided by Arksey and O'Malley's five stages for conducting a scoping review. A comprehensive search of nine databases revealed 1,739 articles. In total, 42 published research papers that focused specifically on TBI secondary to IPV were included in the study. SYNTHESIS: The literature reports inconsistencies in prevalence rates from IPV-related TBI. There are no current standardized screening practices in use, though the literature calls for a specialized tool. Frontline professionals would benefit from education on signs and symptoms of IPV-related TBI. Empirical studies are needed to generate reliable data on prevalence, experience, and needs of brain-injured survivors of TBI. CONCLUSIONS: Findings from this study demonstrate the need for the development of an IPV-sensitive screening tool, more accurate data on prevalence, an interprofessional approach to care, and raised awareness and education on the diffuse symptoms of IPV-related TBI.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".