Making Rural and Remote Communities More Age-Friendly: Experts’ Perspectives on Issues, Challenges, and Priorities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it