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Record W7038545645

Improving Dementia Services for Remote Dwelling Aboriginal People: Report from a Churchill fellowship

2018· article· en· W7038545645 on OpenAlexaboutno aff

Bibliographic record

VenueResearchOnline@ND (The University of Notre Dame) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaIndigenousService providerOlder peoplePresentation (obstetrics)Service delivery frameworkService (business)PhoenixHealth servicesCulturally appropriate
DOInot available

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.967
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.320
Teacher spread0.302 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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