Implementation of a best-practice model of care for cognitive impairment and dementia for first nations peoples attending primary care in Australia: a stepped-wedge cluster-randomised trial
Bibliographic record
Abstract
Background: Dementia and cognitive impairment not dementia (CIND) are under-detected amongst First Nations peoples attending primary care. This trial implemented a culturally adapted best-practice model of care to increase detection and optimise management of CIND/dementia. Methods: This closed cohort open-label, stepped-wedge, cluster-randomised trial recruited 12 Aboriginal community-controlled primary health care services (ACCHSs) across urban, regional and remote settings in Australia. ACCHSs were eligible to participate if they conducted annual health checks, engaged in continuous quality improvement processes and had ≥55 clients aged ≥50 years. After a baseline control period, four ACCHSs were scheduled to enter the intervention phase every six months. During the intervention phase, ACCHSs were supported to embed best-practice dementia care through staff education and practice change initiatives. Co-primary outcomes were: (i) documented detection of CIND/dementia and, (ii) evidence of uptake of the diagnostic pathway measured as presence of ≥2 of: use of cognitive assessment tools, relevant pathology investigations, neuroimaging, and/or referral of clients with cognitive concerns to specialist services. Data were analysed with mixed effects complementary log-log regression. This study was registered with the Australia and New Zealand Clinical Trials Registry, ACTRN12618001485224. Findings: Between September 2018 and January 2019, 12 ACCHSs were recruited, comprising a sample of 1655 ACCHS clients aged ≥50 years (mean 60.3 ± 8.2 years), of whom 935 (56.5%) were female. One ACCHS withdrew during the study. After adjustment for time, the intervention did not show evidence of an effect for the first co-primary outcome (detection of CIND/dementia): HR = 1.53 (95% CI 0.64, 3.65). However, the intervention improved the second co-primary outcome (uptake of diagnostic pathway): HR = 2.34 (95% CI 1.05, 5.25). Intention-to-treat analyses yielded similar results. Interpretation: The co-developed best-practice model of care for cognitive impairment and dementia for Aboriginal and Torres Strait Islander people attending primary care improved the diagnostic CIND/dementia management process. Funding: National Health and Medical Research Council (Australia) and Dementia Training Australia.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".