Age-friendly cities and communities: a review and future directions
Bibliographic record
Abstract
Abstract The unprecedented increase in the ageing population, coupled with urbanisation, has led to a vast number of research publications on age-friendly cities and communities (AFCC). However, the existing reviews on AFCC studies are not sufficiently up-to-date for AFCC researchers. This paper presents a thorough analysis of the annual publication trend, the contributions of authors and institutions from different countries, and the trending research themes in the AFCC research corpus through a systematic review of 98 publications. A contribution assessment formula and thematic analysis were used for the review. The results indicated a growing AFCC research interest in recent times. Researchers and institutions from the United States of America, Canada, United Kingdom and Hong Kong made the highest contribution to the AFCC research corpus. The thematic analysis classified the AFCC research corpus into four main themes: conceptualisation; implementation and development; assessment; and challenges and opportunities. The themes indicate the current and future research patterns and issues to be considered in the development of AFCC and for interested researchers to make proposals for future research. Future directions are proposed, including suggestions on adopting new assessment methods and instruments, collaboration and cross-nation comparative research, considering older adults as place-makers and conducting a prior participatory analysis to maximise the participation of older adults.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".