Identifying and understanding the health and social care needs of Indigenous older adults with multiple chronic conditions and their caregivers: a scoping review
Bibliographic record
Abstract
Abstract Background Indigenous people continue to experience high rates of multiple chronic conditions (MCC) at younger ages than other populations, resulting in an increase in health and social care needs. Those who provide services designed to address MCC for Indigenous communities require synthesized information to develop interventions that meet the needs of their older adult population. This review seeks to answer the research question: What are the health and social care needs, priorities and preferences of Indigenous older adults (living outside of long-term care settings) with MCC and their caregivers? Methods A scoping review, guided by a refinement of the Arksey & O’Malley framework, was conducted. Articles were included if the authors reported on health and social care needs and priorities of older Indigenous adults. We also included articles that focused on Indigenous conceptions of wellness, resilience, well-being, and/or balance within the context of aging, and articles where authors drew from Indigenous specific worldviews, ways of knowing, cultural safety, cultural competence, cultural appropriateness, cultural relevance and community needs. Results This scoping review included 9 articles that were examined using an Indigenous determinants of health (IDH) theoretical framework to analyze the needs of older adults and CGs. Five areas of needs were identified: accessible health services; building community capacity; improved social support networks; preservation of cultural values in health care; and wellness-based approaches. Conclusion The review highlights key determinants of health that influenced older adults’ needs: education and literacy, ethnicity, and social support/network (proximal); health promotion and health care (intermediate); and a combination of historical and contemporary structures (distal). The findings highlight the importance of local Indigenous knowledge and perspectives to improve accessibility of culturally relevant health and social services.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".