Temporalities of emergency: the experiences of Indigenous women with traumatic brain injury from violence waiting for healthcare and service support in Australia
Bibliographic record
Abstract
Globally, traumatic brain injury (TBI) has been recognised as a serious health issue not only because of the immediate impacts at the time the injury occurs but even more so due to the longstanding impacts. Even though TBI is a globally recognised condition, the research is disproportionately focused on its incidence in, and immediate and long-term effects on men. A growing body of research suggests that generally, women who experience family violence are at high risk of TBI and suffer its impacts in ways that reflect gendered differences in the patterns and frequency of violence. In Australia, the social and physical costs of TBI are multiplied for Indigenous women, whose experience of disability and access to healthcare lies at the intersection of gender and race in the historical context of settler colonialism. The present study addresses the need for research into the sociodemographic inequalities that affect access to culturally appropriate hospital care, timely response systems, and flexible, safe and engaged social services. This paper draws on data from interviews and focus groups with Indigenous women, hospital staff and community-based service providers and suggests potential pathways for further research in settler-colonial settings elsewhere in the world.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".