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Record W4398179240 · doi:10.1080/14461242.2024.2345596

Temporalities of emergency: the experiences of Indigenous women with traumatic brain injury from violence waiting for healthcare and service support in Australia

2024· article· en· W4398179240 on OpenAlexaff
Michelle S. Fitts, Karen Soldatić

Bibliographic record

VenueHealth Sociology Review · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTemporalitiesProject commissioningIndigenousPublishingService (business)Health careSociologyTraumatic brain injuryMedical emergencyNursingCriminologyPublic relationsMedia studiesPsychologyMedicinePolitical sciencePsychiatryBusinessLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.213
GPT teacher head0.478
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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