How is Respect and Social Inclusion Conceptualised by Older Adults in an Aspiring Age-Friendly City? A Photovoice Study in the North-West of England
Bibliographic record
Abstract
The World Health Organisation (WHO) Global Age-Friendly Cities (AFC) Guide classifies key characteristics of an AFC according to eight domains. Whilst much age-friendly practice and research have focused on domains of the physical environment, those related to the social environment have received less attention. Using a Photovoice methodology within a Community-Based Participatory Research approach, our study draws on photographs, interviews and focus groups among 26 older Liverpool residents (60+ years) to explore how the city promotes respect and social inclusion (a core WHO AFC domain). Being involved in this photovoice study allowed older adults across four contrasting neighbourhoods to communicate their perspectives directly to Liverpool's policymakers, service providers and third sector organisations. This paper provides novel insights into how: (i) respect and social inclusion are shaped by aspects of both physical and social environment, and (ii) the accessibility, affordability and sociability of physical spaces and wider social processes (e.g., neighbourhood fragmentation) contributed to or hindered participants' health, wellbeing, intergenerational relationships and feelings of inclusion and connection. Our findings suggest that respect and social inclusion are relevant across all eight domains of the WHO AFC Guide. It is core to an AFC and should be reflected in both city-based policies and evaluations.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".