Elements of Long-Term Care That Promote Quality of Life for Indigenous and First Nations Peoples: A Mixed Methods Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Little is known about elements of long-term care (LTC) that promote quality of life (QoL) for older Indigenous and First Nations peoples. This systematic review aimed to extend understanding of those deemed most important. RESEARCH DESIGN AND METHODS: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, systematic database and hand-searching were used to find published and unpublished qualitative studies and textual reports. A convergent integrated approach was used to synthesize data, according to the Joanna Briggs Institute methodology for mixed methods systematic reviews. RESULTS: Included papers (11 qualitative; seven reports) explored views and experiences of Indigenous residents, families, and LTC staff from North America (8), South Africa (1), Norway (1), New Zealand (1), and Australia (7). Elements of care included: (a) codesigning and collaborating with Indigenous and First Nations communities and organizations to promote culturally safe care; (b) embedding trauma-informed care policies and practices, and staff training to deliver culturally safe services; (c) being respectful of individual needs, and upholding cultural, spiritual and religious beliefs, traditional activities and practices; (d) promoting connection to culture and sense of belonging through sustained connection with family, kin, and Indigenous and First Nations communities. DISCUSSION AND IMPLICATIONS: This review identifies elements or models of care that promote QoL for Indigenous and First Nations peoples in LTC. While included papers were mostly from the United States and Australia, the congruence of elements promoting QoL was evident across all population groups. Findings may be used to inform standards specific to the care of Indigenous and 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 | 0.030 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| 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".