Traumatic brain injury as a result of violence for Indigenous women: The importance of appropriate monitoring systems, screening and models of care
Bibliographic record
Abstract
Violence against Indigenous women is a global challenge that few governments have taken effective action to address domestically, regionally and internationally. Governments in Australia, Aotearoa New Zealand, Canada and the United States have developed frameworks for addressing the broader family violence epidemic experienced by women and their children in all cultural groups. As a result of this increased and sustained policy attention, one area that is now receiving greater recognition is the impact of violence-related traumatic brain injury (TBI) on women. TBI is a common injury arising from the repeated and frequent incidence of family violence. Longstanding empirical evidence demonstrates that Indigenous women in Australia, and its sister settler colonial states of Aotearoa New Zealand, Canada and the United States, experience higher rates of family violence resulting in TBI. Unfortunately, this is not unique to settler colonial societies, and increasingly, Indigenous research in the area of gender-based violence suggests that TBI from family violence is highly prevalent for Indigenous women globally. This chapter provides a timely opportunity to reflect on what is known about TBI related to family violence for Indigenous women in Australia and to consider knowledge areas that warrant further attention and their applicability to Indigenous women in Australia and sister settler colonial states, including Aotearoa New Zealand, Canada and the United States.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".