Investigating intimate partner violence as a dementia risk factor through co‐creation of research design and knowledge translation deliverables with survivors and sector partners
Bibliographic record
Abstract
Abstract Background Approximately 30% of women worldwide experience intimate partner violence (IPV). Although as many as 92% report impacts to the head and/or strangulation that raise clinical suspicion of brain injury (BI), there are no evidence‐based methods to document IPV‐BI in this vulnerable population, no clinical practice guideline, and insufficient understanding about long‐term risks including Alzheimer’s Disease and Related Dementias (ADRD). Although traumatic brain injury (TBI) is an established ADRD risk factor, little is known about attributable risk of ADRD due to IPV in either military or civilian populations. Our study aims to improve diagnosis of BI in survivors of IPV in a manner sensitive to the needs of this vulnerable population, and to raise awareness of the importance of considering IPV‐BI as a potential ADRD risk factor. Methods We were recently approved by the USA Department of Defence to launch a prospective observational study that leverages collaborative research at three clinical sites in British Columbia Canada that reach ethnically and geographically diverse participants. We will assess acute and chronic plasma biomarkers as diagnostic tools and will also use an integrated knowledge translation (iKT) framework including community participation to deliver a living clinical practice guideline for IPV‐BI and trauma‐informed guidance for ADRD researchers to specifically ask about IPV in clinical history. Results Thus far, we have worked with multiple stakeholders to co‐create a novel Case Report Form suitable for use in real‐life clinics to collect IPV history including head impact and strangulation events, and have pilot data on several plasma biomarkers relevant to ADRD that, for some cases, suggest IPV may lead to accelerated brain aging and biomarker patterns consistent with AD. Conclusions We will discuss how extensive feedback from persons with lived experience and stakeholder groups was essential to design a feasible community‐based study, as common data elements now almost routine in ADRD and TBI studies needed to be reworked to consider the unique needs of IPV survivors in participating in research.
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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 | 0.144 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".