Gearing towards Sustainable Indigenous Elderly Quality of Life: A Systematic Review
Bibliographic record
Abstract
Indigenous elderly is one of the most medically underserved groups and are often left out, which resulted in significant health disparities. Besides, research on the indigenous elderly health and wellbeing is somewhat limited despite the world slogan of health equity for social sustainability. Hence, having to assess the health status of the group will make a balanced effort for improving their overall quality of life. This paper aims to evaluate and synthesise the indigenous elderly health and wellbeing by using the systematic review of Scopus-indexed publications, published from 2003 to November 2020. Ninety-three articles served as the initial data, but only 22 articles were eligible to be used for the analyses of 1) perception and beliefs on health behaviour, 2) physical and mental health practices, 3) impactful studies and 4) implications of the studies towards policy and healthcare delivery. The findings revealed developed countries like the USA, Canada, and New Zealand have placed greater efforts in voicing out the group needs. Suggestions for future research are to focus on intervention programs and improvement of the healthcare policy development for the indigenous elderly ease of access to healthcare, fundamental for reducing the social gaps.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".