Best practice models of Aged care implemented for First Nations people: a systematic review aligned with the Good Spirit Good Life quality of life principles
Bibliographic record
Abstract
Abstract Development and delivery of aged-care programs based in a First Nations worldview to First Nations Elders is believed to translate to improved quality of life. First Nations perspectives of health and well-being incorporates social and cultural determinants in addition to traditional Western biomedical approaches. The Australian Royal Commission into Aged Care which identifies a need for culturally-appropriate aged care represents a strong policy driver in undertaking this work. We undertook a systematic review of the available evidence regarding implementation of culturally appropriate measures into models of practice. The sixteen included articles were systematically assessed and thematically analysed. The Good Spirit Good Life (GSGL) tool consists of 12 strength-based factors determined by First Nations Elders as constituting culturally appropriate aging. The publications included in this review were assessed as demonstrating a strong alignment with these 12 factors and a general consensus surrounding the constituents of aging well for First Nations Elders. From our findings, we identified that best-practice regarding First Nations ageing requires a decolonising approach involving top-down systematic change within organisations. This review contributes to an understanding of the enablers of best-practice models of care and supports determining strategies for the effective implementation of the 12 GSGL factors.
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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 | 0.040 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".