Social inclusion programming for older adults living in age-friendly cities: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Creating age-friendly cities (AFCs) is essential for supporting older adults' well-being. The WHO's 2007 guide outlines key features of AFCs, including social inclusion. Despite increasing numbers of AFC programmes, diverse experiences of ageing are often overlooked. This scoping review explores innovative programmes implemented by AFCs to enhance social inclusion for older adults. DESIGN: A scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. DATA SOURCES: Systematic searches were conducted between December 2023 and January 2024 across relevant databases (Ovid Medline, OVID Embase, OVID PsycINFO, CINAHL, Web of Science, Cochrane Library and Scopus) and the grey literature. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Selection criteria included English language publications describing evaluated age-friendly, social inclusion programmes for older adults. DATA EXTRACTION AND SYNTHESIS: Data extraction followed Gonyea and Hudson's (2015) framework assessing programmes on population, environment and/or sector inclusion levels. Inductive analysis identified and evaluated aspects of social inclusion. RESULTS: We identified 20 peer-reviewed publications and 18 grey literature sources. Most programmes (peer review, n=19, 95.0%; grey, n=18, 100.0%) addressed population inclusion, incorporated environment (peer review, n=10, 50.0%; grey, n=15, 83.3%) and/or sector inclusion (peer review, n=7, 35.0%; grey, n=15, 83.3%). Key outcomes included an improved sense of belonging, increased engagement with community resources and activities, enhanced digital literacy and connectivity, and a reduction in feelings of loneliness and isolation. A notable gap was the absence of studies focused on Indigenous populations. CONCLUSION: We highlight that programmes addressing population, environment and sectoral inclusion may improve the well-being of older adults in urban settings. Our findings will inform AFC practices and policies by deepening our understanding of how social inclusion can be improved for older adults, including those from under-represented groups, ensuring an equitable approach to enhancing quality of life.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".