Frailty in Indigenous Populations: A Scoping Review
Bibliographic record
Abstract
Background: Indigenous populations experience high rates of age-related illness when compared to their non-Indigenous counterparts. Frailty is a challenging expression of aging and an important public health priority. The purpose of this review was to map what the existing literature reports around frailty in Indigenous populations and to highlight the current gaps in frailty research within the Indigenous landscape. Method: Scoping review of English language original research articles focusing on frailty within Indigenous adult populations in settler colonial countries (Australia, Canada, New Zealand and USA). Ten electronic databases and eight relevant institutional websites were searched from inception to October 2020. Results: Nine articles met our inclusion criteria, finding this population having a higher prevalence of frailty and frailty occurring at younger ages when compared to their non-Indigenous counterparts, but two did not use a formal frailty tool. Females presented with higher levels of frailty. No culturally specific frailty tool was identified, and the included articles did not assess strategies or interventions to manage or prevent frailty in Indigenous peoples. Conclusions: There was little definitive evidence of the true frailty prevalence, approaches to frailty screening and of potential points of intervention to manage or prevent the onset of frailty. Improvements in the quality of evidence are urgently needed, along with further research to determine the factors contributing to higher rates of frailty within Indigenous populations. Incorporation of Indigenous views of frailty, and instruments and programs that are led and designed by Indigenous communities, are crucial to address this public health priority.
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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 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| 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) | 0.003 | 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".