Traumatic Brain injury From intimate Partner Violence : Understanding The Foundations of a Health inequity
Bibliographic record
Abstract
True prevalence of traumatic brain injuries (TBI) in the context of intimate partner violence (IPV) remains unknown given the hesitancy of women in abusive relationships to disclose abuse and to seek medical treatment unless the abuse is severe. Research estimates that 75% of women with a history of IPV have sustained TBI from IPV with nearly 50% women reporting receiving multiple TBI. When women do seek treatment for TBI or IPV, they must choose between a womenu2019s shelter where they will not receive medical treatment or a clinical setting where they may not feel safe from the abuser.To understand the full nature and context of a woman receiving TBI during an episode of IPV it is integral to think through levels and across sectors, including personal and social risk factors for violence and abuse and missed opportunities to access resources. While health disparities like these are becoming more widely acknowledged, the siloing of TBI and violence research and policy has obscured the reality that TBI from IPV is better understood as a health equity issue: one in which the health disparities are largely avoidable. Three concepts will be used to aid in describing TBI from IPV as a health inequity: intersectionality, syndemics, and structural violence. Intersectionality explains how the interactions between identities of race, class, ability, and gender affect individual experiences, opportunities, and social value. Structural violence is a phenomenon wherein a policy, structure, or institution prevents someone from accessing resources to meet their needs. Syndemics can be described as the ways in which two or more diseases interact in social conditions to create an excessive burden on health. These concepts are defined and explained as an equation to visualize how individual-level labels and characteristics (intersectionality) interact with sociocultural systems-level discrimination (structural violence) to lead to increased health risk and burden in communities (syndemics). Canadian First Nation and Inuit women will be used as case studies to demonstrate intersectionality of characteristics leading to increased risk for TBI from IPV in communities. Violence and trauma prevention must be priorities at community and policy levels with approaches that are tailored to account for the fact that certain populations are at increased risk for multiple TBI. This theoretical reframing can lead to developing a more nuanced operationalization of structural violence and trauma-informed care with implications for research, practice, and policy for women living with TBIs from IPV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| 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".