Expanding the Circle of Knowledge: Reconceptualizing Successful Aging Among North American Older Indigenous Peoples: Table 1.
Bibliographic record
Abstract
OBJECTIVES: Indigenous older peoples' voices and experiences remain largely absent in the dominant models and critical scholarship on aging and late life. This article examines the relevance of the model of successful aging for Indigenous peoples in North America. METHOD: This article presents the results of a review of the published conceptual literature on successful aging among Indigenous peoples. Our intent was to explore the current state of the field of successful aging among Indigenous peoples and suggest dimensions that may be more reflective of Indigenous voices and experiences that leads to a more inclusive model of successful aging. RESULTS: Based on our review, we suggest four dimensions that may broaden understandings of successful aging to be more inclusive of Indigenous older people: health and wellness, empowerment and resilience, engagement and behavior, and connectedness. DISCUSSION: Our review suggests that Indigenous peoples' voices and experiences are beginning to be included in academic literature on successful aging. However, we suggest that understandings of successful aging be broadened based on our summative findings and a process of community involvement. Such processes can lead to the development of models that are more inclusive to a wide range of older people, including Indigenous older 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".