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Record W4390192291 · doi:10.1002/alz.077977

Culturally relevant protective factors and relationships with cognitive decline in First Nations Australians

2023· article· en· W4390192291 on OpenAlexaboutno aff
Louise Lavrencic, Holly A. Mack, Adrienne Withall, Kim Delbaere, G. Anthony Broe, Gail Daylight, Brian Draper, Danielle Lasschuit, Kylie Radford

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaPsychosocialGerontologyIndigenousCognitionPopulationEducational attainmentPsychologyCognitive declineMedicinePsychiatryEnvironmental healthDiseasePolitical science

Abstract

fetched live from OpenAlex

Abstract Background First Nations people in Australia experience dementia prevalence 3‐5 times higher than the broader Australian population. There is strong focus on risk factors (particularly biomedical) in dementia research; yet further research is required to understand protective factors, especially psychosocial and cultural determinants. Cognitive reserve (CR) refers to the brain’s adaptability when faced with age or neurodegenerative‐related changes. CR is important for understanding cognitive outcomes and dementia onset. Psychosocial factors are thought to contribute to CR, including education and work opportunities. Yet, the appropriateness of conventional CR measures for First Nations people, and the role of culturally‐specific protective factors in CR, is unclear. Method Data were from the Koori Growing Old Well Study, a longitudinal, population‐based study with First Nations Australians aged 60+ from 5 rural/urban communities in New South Wales (N = 336 baseline; N = 165 6‐year follow‐up, n = 68 died before follow‐up). Comprehensive and holistic interviews were carried out. Dementia and mild cognitive impairment (MCI) were based on clinical assessment and consensus review. CR factors spanned education and work opportunities, sociocultural activities/connections, creative hobbies, playing sports, and social contact. Logistic regression analyses were used to examine factors at each life stage (early/mid/late‐life) in relation to cognitive decline (incident MCI/dementia). Result The analyses included 155 participants (excluding N = 10 with baseline dementia). Of the early‐life factors, singing (from the Retrospective Indigenous Childhood Enrichment Scale) (OR = 0.33; 95% CI 0.13, 0.86; p = .024) was associated with a lower risk of cognitive decline in the multivariable model; and years of education (OR = 0.79; 95% CI 0.66, 0.94; p = .008) predicted cognitive decline in univariable analysis. For midlife, history of unskilled work was associated with cognitive decline (OR = 5.44; 95% CI 2.32, 12.75; p = <.001). No variables in the late‐life model were significantly associated with cognitive decline. Conclusion This study is the first to examine factors that may contribute to CR in First Nations communities, including culturally‐specific items. These results suggest that certain life‐course experiences may protect against cognitive decline; however, revised and sensitive measures are needed to capture nuanced differences. A culturally relevant and more comprehensive CR measure is currently being piloted with First Nations people.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.058
GPT teacher head0.327
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2023
Admission routes1
Has abstractyes

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