Screening for History of Traumatic Brain Injury Among Women Exposed to Intimate Partner Violence
Bibliographic record
Abstract
Traumatic brain injury (TBI) is a common chronically debilitating consequence of intimate-partner violence (IPV). Diagnosis and effective treatment are precluded by poor detection and lack of uniform practice guidelines for TBI screening in IPV. Although there are several TBI-screening tools commonly used in clinical and research practices, their applicability to this unique and vulnerable population is unclear. In this review paper, we propose a theoretically based framework for screening for history of TBI in women exposed to IPV and apply it to investigate the applicability of TBI-screening instruments. The framework was developed by examining existing guidelines for working with IPV survivors and applied to evaluate the content of nine currently available TBI screening instruments to determine the extent to which each offers (1) events that can lead to TBI in an IPV situation; (2) safe (without increasing the risk of retaliation) endorsement of an event; and (3) ease of administration. Our evaluation of the currently available TBI-screening tools determined that no instrument met the proposed framework standards and only 2 (Brain Injury Screening Questionnaire and Ohio State University TBI Identification Method) came close, requiring only minor adjustments to meet the postulated criteria. We make specific content and interview-based recommendations for revising TBI screening instruments to minimize the weaknesses of currently available screening tools among women exposed to IPV and the knowledge gaps about TBI in this context. The proposed framework and recommendations are intended to guide future work in this area to enhance the capacity of TBI screening tools to safely detect TBI in this population. LEVEL OF EVIDENCE: V.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".