Aging all over the place: a multidisciplinary framework that considers place and life trajectories of older adults within their communities
Bibliographic record
Abstract
Purpose This conceptual paper aims to describe aging all over the place (AAOP), a federative framework for action, research and policy that considers older adults’ diverse experiences of place and life trajectories, along with person-centered care. Design/methodology/approach The framework was developed through group discussions, followed by an appraisal of aging models and validation during workshops with experts, including older adults. Findings Every residential setting and location where older adults go should be considered a “place,” flexible and adaptable enough so that aging in place becomes aging all over the place . Health-care professionals, policymakers and researchers are encouraged to collaborate around four axes: biopsychosocial health and empowerment; welcoming, caring, mobilized and supportive community; spatiotemporal life and care trajectories; and out-of-home care and services. When consulted, a Seniors Committee showed appreciation for flexible person-centered care, recognition of life transitions and care trajectories and meaningfulness of the name. Social implications Population aging and the pandemic call for intersectoral actions and for stakeholders beyond health care to act as community leaders. AAOP provides opportunities to connect environmental determinants of health and person-centered care. Originality/value Building on the introduction of an ecological experience of aging, AAOP broadens the concept of care as well as the political and research agenda by greater integration of community and clinical actions. AAOP also endeavors to avoid patronizing older adults and to engage society in strengthening circles of benevolence surrounding older adults, regardless of their residential setting. AAOP’s applicability is evidenced by existing projects that share its approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| 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".