Detecting a hidden pandemic: The current state and future direction of screening and assessment tools for intimate partner violence-related brain injury
Bibliographic record
Abstract
Intimate partner violence (IPV) is a major global concern, and IPV victim-survivors are at an increased risk of brain injury (BI) due to the physical assaults. IPV-BI can encompass both mild traumatic brain injury (mTBI) and non-fatal strangulation (NFS), but IPV-BI often goes undetected and untreated due to a number of complicating factors. Therefore, the clinical care and support of IPV victim-survivors could be enhanced by BI screening and assessment in various settings (e.g., first responders, emergency departments, primary care providers, rehabilitation, shelters, and research). Further, appropriate screening and assessment for IPV-BI will support more accurate identifications, and prevalence estimates, improve understanding of health implications, and have the potential to inform policy decisions. Here we overview the seven available tools that have been used for IPV-BI screening and assessment purposes, including the BISA, BISQ-IPV, BAT-L/IPV, OSU TBI-ID, the HELPS, and the CHATS, and outline the advantages and disadvantages of these screening tools in the clinical, community, and research settings. Recommendations for further research to enhance the validity and utility of these tools are also included.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".