Age-Friendly Cities and Older Indigenous People: An Exploratory Study in Prince George, Canada
Bibliographic record
Abstract
Cities around the world are responding to aging populations and equity concerns for older people by developing age-friendly communities plans, following the World Health Organization's guidelines. Such plans, however, often fail to account for the wide diversity of older people in cities, with the result that some older people, including Indigenous older people, do not see their needs reflected in age-friendly planning and policies. This article reports on a study involving 10 older First Nations and Métis women in the city of Prince George, Canada, comparing the expressed needs of these women with two age-friendly action plans: that of the city of Prince George, and that of the Northern Health Authority. Four main categories were raised in a group discussion and interview with these women at the Prince George Native Friendship Centre: availability of health care services, accessibility and affordability of programs and services, special roles of Indigenous Elders, and experiences of racism and discrimination. There are many areas of synergy between the needs expressed by the women and the two action plans; however, certain key areas are missing from the action plans; in particular, specific strategies for attending to the needs of Indigenous and other older populations who often feel marginalized in health care and in age-friendly planning.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.026 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".