HOUSING CONCERNS AND TRAJECTORIES OF KINLESS OLDER ADULTS WITH DEMENTIA
Bibliographic record
Abstract
Abstract Housing preferences of older adults (e.g., to remain in residence, or relocate), housing options available, and housing needs related to changing physical and cognitive abilities are common concerns of aging. The challenges are possibly more complex for those living with dementia, who may have difficulty articulating housing preferences, and whose housing needs may include supportive care. For those with dementia who lack close kin, these challenges are likely significantly compounded. This paper focuses on the intersection of housing concerns for 64 participants in the Adult Changes in Thought (ACT) study who were kinless (no living spouse or children) when they developed dementia. All the ACT participants were 65+ when recruited. Everyone in this sample had received a research diagnosis, and their mean age at dementia onset (estimated at the midpoint between the research study that triggered the diagnostic evaluation, and the last research study before that -- usually one year before diagnosis) was 87 (Standard Deviation [SD] 7 years), with a median age of 86 and a range of 71-103.We present an analysis of their housing trajectories, as captured in chart notes from their medical records. We highlight multiple themes illustrating how housing intersects with health and social well-being and conclude by commenting on the potential of medical records for understanding and documenting issues related to housing.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".