An integrative review of the factors related to building age‐friendly rural communities
Bibliographic record
Abstract
AIM AND OBJECTIVES: To identify the theories and concepts related to building age-friendly rural communities. BACKGROUND: Global population is rapidly ageing. Creating environments that support active ageing was a catalyst for the World Health Organization to develop Global Age-Friendly Cities guidelines. Although the age-friendly movement has captured the attention of some countries, little is known about the participation of older people in rural settings. METHOD: An integrative review approach was employed to summarise the research literature on this topic. Using a systematic search strategy, databases including Discover (EBSCO's electronic database system), Web of Science, Scopus, PubMed, CINAHL, PsycINFO, Medline and Google Scholar were searched. Primary, peer-reviewed studies were included if published during 2007-2014 in the English language. RESULTS: Nine studies were eligible for inclusion. The studies were set predominantly in Canada, with the exception of one from Ireland. The findings were summarised and clustered into main topics which included: theoretical perspectives; geographic and demographic characteristics; collaboration and partnerships; sustainability and capacity; and finally, future research agendas. CONCLUSIONS: Rural communities are changing rapidly and are becoming increasingly diverse environments. Community characteristics can help or hinder age-friendliness. Importantly, the fundamental starting point for age-friendly initiatives is establishing older peoples' perceptions of their own communities. RELEVANCE TO CLINICAL PRACTICE: It is important for nurses, working in primary health care settings, to understand the needs of older people in the communities in which they practice. This includes the community characteristics that can be enablers and barriers to older people being able to remain and age within their own communities.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".