Contextual Factors for Aging Well: Creating Socially Engaging Spaces Through the Use of Deliberative Dialogues
Bibliographic record
Abstract
Purpose of the Study: Home and community engagement are key contextual factors for aging well, particularly for older adults in vulnerable social positions. A community-based participatory action research project conducted in Western Canada examined how to best use the shared amenity spaces in a low-income seniors' apartment complex in order to connect services and programs with tenants and to provide opportunities for service providers and local stakeholders to build upon and create new relationships toward collaboration and service delivery. Design and Methods: Pre-move deliberative dialogue workshops (n = 4) were conducted with stakeholders (e.g., service providers, developers, and municipal government employees). Workshop participants (n = 24) generated ideas and plans on how physical and social environments can contribute to the social engagement of senior tenants. Results: Shared dialogue led to community investment and asset sharing by integrating the knowledge and experiences of multiple stakeholder groups into the planning process. This article highlights how collaborative planning approaches for the effective use of the social environment (e.g., social programming), within the physical environment (e.g., amenity and community spaces), can generate rich and illuminating data for informing enhancements in the social environment of apartment dwelling low-income seniors. Contextual challenges to service provision are discussed, including the need for communication about and coordination of on-site programming, culturally diverse and responsive programming, and long-term funding. Implications: Prolonging independent community living with the assistance of support services should be a goal to both delay premature relocation into institutional care and meet the preferences 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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".