“Why do i have to get naked to have a sandwich made?”: The social problem of aging and cultural shift needed to facilitate aging in place
Bibliographic record
Abstract
BACKGROUND: Over the next 2 decades, the population of older adults (65 years of age and up) in Canada is anticipated to rise significantly, with the most rapidly increasing subset being racialized minorities. Many older adults prefer to age at home and in the community despite lacking services to support this goal. Considering older adults' growth rate and preferences, the research team sought to investigate what resources were needed to facilitate aging in place. METHODS: This study took place in Peel Region (Ontario, Canada), a particularly diverse region in Canada. 15 focus groups and 7 1-on-1 interviews were conducted with 42 participants (14 older adults, 10 caregivers, 18 healthcare providers). Each session included 2 parts: (1) co-designing a persona, and (2) co-designing a care package for the persona based on what they would need to age at home. A collaborative approach was taken to Braun and Clarke's inductive thematic analysis. FINDINGS: Overall, participants pointed to the social problem and medicalization of aging and discussed a broader cultural shift necessary to facilitate aging in place. This cultural shift was multifaceted and involved reducing fear-based health education rooted in liability concerns, caregivers and families accepting risks, a better balance between governmental responsibility and community sustainability, and looking to other optimal models of care (e.g., palliative care model, family planning model for advanced care planning). Important to shifting the culture of care was also the need to better balance standardized and personalized care. Older adults and caregivers needed opportunity to voice their needs. When older adults did not receive flexible accommodations for their specific circumstances, there were often spillover costs. Finally, important to centering older adults and caregivers in person-centred care provision was a needed shift in the consistency and reliability of care services that supported older adults to age in place. CONCLUSIONS: Participants described receiving care that was often not person- and family-centered despite this being a purported healthcare system value in Ontario, Canada. A cultural shift is thus needed in medical and social expectations of what it means to care for 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.019 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".