Strategies that support cultural safety for First Nations people in aged care in Australia: An integrative literature review
Bibliographic record
Abstract
OBJECTIVE: The 2019 Royal Commission into Aged Care Quality and Safety highlighted the need for First Nations peoples to have improved, culturally safe care. This paper is a call to action for First Nations peoples to be involved in developing culturally safe care and services to be embedded within Australian aged care services. METHODS: The first screening examined the Australian literature (peer-reviewed articles published since 2010 in English) detailing key aspects relevant to Cultural Safety for First Nations peoples supported by aged care services in Australia. The second screening assessed whether the findings of these studies aligned with the key aspects of Cultural Safety of First Nations peoples in aged care. RESULTS: The initial literature search yielded 198 papers, of which 13 met the inclusion criteria for the final review. Topics that required further interrogation included barriers to communication, racism and discrimination, impacts on health outcomes, health-care workforce education needs and the importance of cultural connections to Country and kin. These topics influenced the perception of First Nations peoples feeling culturally safe when supported by aged care services. CONCLUSIONS: The literature identified a need to recruit more First Nations peoples into the aged care workforce, involve more First Nations family and community members in aged care and retain a consistent workforce overall. Together these strategies were seen to address the barriers that continue to affect aged care provision for First Nations peoples.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".