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
BACKGROUND: Aged-care programs that are based in First Nations worldviews are believed to translate to improved quality of life for First Nations Elders. First Nations perspectives of health and well-being incorporates social and cultural determinants in addition to traditional Western biomedical approaches. This is exemplified by the Good Spirit Good Life (GSGL) framework, which comprises 12 strength-based factors determined by First Nations Elders as constituting culturally appropriate ageing. Our objective was to conduct a systematic review of existing aged care models of practice to determine the degree of alignment with the GSGL framework. Recommendations of the national Australian Royal Commission into Aged Care Quality and Safety informed this work. METHODS: We conducted a systematic search of academic and grey literature in the PubMed, Scopus, Ovid Embase, and Informit online databases. Inclusion criteria comprised English language, original research describing the implementation of First Nations culturally appropriate aged care models, published before August 2022. Research that was not focused on First Nations Elders' perspectives or quality of life was excluded. We subsequently identified, systematically assessed, and thematically analyzed 16 articles. We assessed the quality of included articles using the Aboriginal and Torres Strait Islander Quality Assessment Tool (ATSIQAT), and the Joanna Briggs Institute (JBI) critical appraisal tool for qualitative research. RESULTS: Most studies were of medium to high quality, while demonstrating strong alignment with the 12 GSGL factors. Nine of the included studies detailed whole service Models of care while 7 studies described a single program or service element. Thematic analysis of included studies yielded 9 enablers and barriers to implementing models of care. CONCLUSIONS: Best-practice First Nations aged care requires a decolonizing approach. Programs with strong adherence to the 12 GSGL factors are likely to improve Elders' quality of life.
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 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 | 0.076 | 0.240 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".