Quality of life instruments for survivors of brain injury from intimate partner violence: a scoping review
Bibliographic record
Abstract
Background: Brain injury related to intimate partner violence (IPV) is a prevalent, yet under-detected public health issue, which has been exacerbated during the COVID-19 pandemic. Brain injury is the reason for a significant portion of emergency department visits, which provides an opportunity to connect with survivors of IPV-related brain injury. Survivors of IPV-related brain injury are a unique population with complex circumstances and outcomes, and therefore require a comprehensive quality of life (QOL) assessment using measures that have been validated for this specific population. Methods: This scoping review searched for QOL instruments used with survivors of IPV-related brain injury. Following the PRISMA guidelines for scoping reviews, we searched six databases for studies measuring QOL in survivors of IPV-related brain injury using Boolean search terms. Results: The initial review revealed no studies evaluating QOL among survivors of IPV-related brain injury, which suggests a significant gap in the literature. The inclusion criteria were therefore modified to include studies with participants who had survived IPV with physical violence. Using the revised criteria, the subsequent review produced four studies, of which three used the Medical Outcomes Study Short-Form 36-item. Conclusions: This review highlights an overall lack of validation of QOL instruments in this population and identifies two measures that have been used in studies with participants who survived physical IPV. Future research should examine the validity and reliability of QOL instruments among survivors of IPV-related brain injury to promote a strengths-based research and practice agenda.
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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 | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".