Healthy aging interventions: exploring community partnerships to support Indigenous well-being
Bibliographic record
Abstract
A program named ‘Walking Our Way to Wellness’ was a healthy aging intervention developed and implemented in a rural Indigenous community. Focusing on interventions are valued and connected through both cultural and community models. This project emphasized the intergenerational aspects of health interventions. The research aimed to explore healthy aging practices, prevalent health conditions, health needs, and the impacts of a community partnership model involving a local post-secondary institution in Nova Scotia. This qualitative study included 17 participants who completed semi-structured interviews about their health and well-being. The mean age of the sample was 67 years, with 11 participants residing within the Indigenous community. A series of health interventions were developed based on participants' needs and interests through a community partnership involving a local Indigenous community, Cape Breton University Education Department, and the School of Nursing. These interventions included nutrition education, diabetes education, a walking program, and functional fitness programming. The results revealed that arthritis and diabetes were the most commonly reported health conditions among participants. Participants also noted their engagement in new functional fitness exercises and expressed that social interactions and scheduled classes were beneficial for their overall well-being. Suggestions for future research are also provided, which aim to build on the successes of this intervention and further explore the benefits of culturally grounded, community-driven health initiatives. Keywords: Aging, health condition, innovation in community health, nursing, partnership
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 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".