Aging in the Right Place: A Conceptual Framework of Indicators for Older Persons Experiencing Homelessness
Bibliographic record
Abstract
Aging in place may not be a universally optimal goal nor accessible to all. Research has highlighted the significance of aging in the right place (AIRP) by recognizing that secure housing for older adults should support one's unique vulnerabilities and lifestyles. Despite the evolving conceptualization of AIRP for general populations of older adults, considerations of AIRP relevant for older people with previous or current experiences of homelessness are absent from the existing literature. Given this conceptual gap, we developed a framework of indicators relevant for older persons experiencing homelessness. We engaged community partners in the development of our framework and examined what had been described in prior research on aging in place and person-environment fit for older adults. The resulting conceptual framework is comprised of 6 subcategories of indicators: (a) built environment of the housing unit and surrounding neighborhood, (b) offsite and onsite health and social services and resources, (c) social integration, (d) stability and affordability of place, (e) emotional place attachment, and (f) broader political and economic contexts. This framework provides a practical and meaningful contribution to the literature which can be used to promote AIRP for individuals whose experiences are often not reflected in existing models.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".