Improving Dementia Services for Remote Dwelling Aboriginal People: Report from a Churchill fellowship
Bibliographic record
Abstract
This research project was inspired by a want to improve dementia services for Aboriginal people residing in remote Aboriginal communities throughout Australia. The award of the Bob and June Prickett Churchill Fellowship enabled my travel to London, Phoenix Arizona, Ontario Canada and Aotearoa. I visited researchers, service providers and communities to explore the provision of dementia services to remote Indigenous populations. This presentation describes the services and researchers I visited, what I learned and how my experiences can be used to improve dementia services for Aboriginal people in remote Australia The aim of my fellowship was to improve dementia services for remote dwelling Aboriginal people. Dementia rates are increasing nationally and the burden of disease affects those with the condition and their families in many ways. Australian Aboriginal people over 45 years are 3- 5 times more likely to be diagnosed with dementia than the non-Aboriginal population. Australian researchers have conducted landmark studies into the development of a culturally appropriate screening tool for dementia in Aboriginal people and tested different service delivery models for older remote-dwelling Aboriginal people and their caregivers. However, these have not been widely adopted. This project enabled me to explore dementia services and research in Indigenous populations in similar contexts.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".