Co-creating inclusive spaces and places: Towards an intergenerational and age-friendly living ecosystem
Bibliographic record
Abstract
Introduction: Evolving aging societies, ongoing digitalisation and circumstances of COVID-19 are changing living conditions for growing older. There is an increased urgency to view public health with a focus on integrating people of all ages into the matrix of opportunities afforded in their communities. This study initiates the conceptualization of an intergenerational, age-friendly living ecosystem (AFLE) to enhance public health planning. Methodology: = 130) were conducted to conceptualize multilevel ideas for building intergenerational age-friendly places using Bronfenbrenner's ecological systems model. At the height of COVID-19, virtual applications (Zoom, Moodboard) and case studies, creative methods (drawing, photography, storytelling and spotlight sessions) were applied to engage academic and non-academic participants between ages 5 - 80+ years, across eight countries. Sessions were video-recorded with visual themes captured by a graphic facilitator. The survey covered issues of multigenerational interactions; intergenerational and age-friendly place features; place safety; and necessary stakeholders required for creating intergenerational and age-friendly places. Data were reflexively analyzed using a team approach to thematic analysis. Results: Findings present both the thematic analysis of Virtual Co-creation Camps (VCCs) and the INTERGEN survey results. These findings are addressed in three overarching categories that highlight the necessary characteristics of AFLEs as suggested by the VCC participants and survey respondents: (i) Sensory factors: feeling and emotion as starting points for physical design; (ii) Physical and digital factors in designing AFLE spaces and places; and (iii) Socio-cultural factors: tackling ageism and exclusion as part of the solution. Discussion: The analysis resulted in a pathway toward enhanced understandings on how multi-generations can better interact with fluctuating organizational domains (industry, voluntary, academic and public sectors) in urban and rural settings to facilitate intergenerational connectivity. Through processes of co-creation, an AFLE proof of concept and roadmap for public health planning was developed to support and provide opportunities for people as they age to reap the socioeconomic benefits of their local and virtual communities and help them become well integrated, valued and contributory members of society.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".