Cognitively Impaired Older Adults: Risk Profiles for Institutionalization
Bibliographic record
Abstract
BACKGROUND: This study focused on the identification of risk profiles for institutionalization among older adults diagnosed with cognitive impairment-not dementia or dementia in 1991/92 and subsequent institutionalization in the following 5-year period. METHODS: Data were from a sample of 123 individuals aged 65+ and their unpaid caregivers in Manitoba, Canada. Cluster analysis was conducted using baseline characteristics of age, cognition, disruptive behaviors, ADLs/IADLs, use of formal in-home services, and level of caregiver burden. RESULTS: Three distinct groups emerged (high risk [n = 12], medium risk [n = 40], and low risk [n = 71]). The high-risk group had the poorest cognitive scores, were the most likely to exhibit disruptive behaviors, were the most likely to need assistance with ADLs and IADLs, and had the highest level of burden among their caregivers. Follow-up of the groups validated the risk profiles; 75% of the high-risk group were institutionalized within the next 5 years, compared to 45% of the medium-risk group and 21% of the low-risk group. DISCUSSION: The risk profiles highlight the diversity among individuals with cognitive impairment and the opportunity for differential targeting of services for the distinct needs of each group.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".