Communication services for First Nations peoples after stroke and traumatic brain injury: Alignment of Sustainable Development Goals 3, 16 and 17
Bibliographic record
Abstract
PURPOSE: Colonisation and continuing discrimination have significantly and negatively impacted the physical, social and emotional wellbeing of First Nations peoples globally. In Australia, Aboriginal cultures thrive despite ongoing barriers to health care. This paper describes challenges and new initiatives for Australian Aboriginal people with acquired communication disability after brain injury and their alignment with the global aims forming the Sustainable Development Goals. RESULT: Research undertaken by an Aboriginal and non-Aboriginal multidisciplinary team over a decade in Western Australia identified and responded to mismatches between community needs and services. Initiatives described include the Missing Voices, Healing Right Way, Brain Injury Yarning Circles and Wangi/Yarning Together projects. Recommendations implemented related to (a) greater incorporation of Aboriginal cultural protocols and values within services, (b) more culturally secure assessment and treatment tools, (c) support after hospital discharge, (d) Aboriginal health worker involvement in support. Implementation includes cultural training of hospital staff, trialling new assessment and treatment methods, and establishing community-based Aboriginal Brain Injury Coordinator positions and relevant peer support groups. CONCLUSION: Culturally secure brain injury rehabilitation in Australia is in its infancy. Our initiatives challenge assumptions about worldviews and established Western biomedical models of healthcare through incorporating Indigenous methodologies and leadership, and community-driven service delivery. This commentary paper focuses on Sustainable Development Goals 3, 16 and 17.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".