Aging in Place: Challenges of Older Adults with Self-Reported Cognitive Decline
Bibliographic record
Abstract
BACKGROUND: An emergent concern related to the aging and the increased risk of cognitive decline is the institutionalization of older adults. Evidence has shown that aging in place leads to many benefits, including higher quality of life. In order to support older adults, it is imperative that we understand the challenges people with changes in cognition face while aging in place. METHODS: A total of sixteen older adults with self-reported cognitive decline and six informal caregivers of individuals reporting cognitive decline, all of whom are living in independent residences, participated. Focus group sessions with semi-structured interviews were conducted, followed by thematic qualitative data analyses. RESULTS: Thematic analyses led to the identification of six challenges to aging in place, including: 1) memory decline, 2) emotional challenges/low mood, 3) social isolation/loneliness, 4) difficulty with mobility and physical tasks, 5) difficulties with activities of daily living/instrumental activities of daily living, and 6) lack of educational resources on cognitive change. CONCLUSION: The themes identified in the current study represent common challenges in aging in place for older adults with self-reported cognitive decline. Identification of these themes allows for important next steps, which can focus on supports through targeted interventions.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".