Making Rural and Remote Communities More Age-Friendly: Experts’ Perspectives on Issues, Challenges, and Priorities
Bibliographic record
Abstract
With the growing interest worldwide in making communities more age-friendly, it is becoming increasingly important to understand the factors that help or hinder communities in attaining this goal. In this article, we focus on rural and remote communities and present perspectives of 42 experts in the areas of aging, rural and remote issues, and policy who participated in a consensus conference on age-friendly rural and remote communities. Discussions highlighted that strengths in rural and remote communities, such as easy access to local leaders and existing partnerships, can help to further age-friendly goals; however, addressing major challenges, such as lack of infrastructure and limited availability of social and health services, requires regional or national government buy-in and funding opportunities. Age-friendly work in rural and remote communities is, therefore, ideally embedded in larger age-friendly initiatives and supported by regional or national policies, programs, and funding sources.
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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 | 0.051 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".