Dementia and Assisted Living 1 Running head: COMPARISON OF QUALITY OF LIFE IN DEMENTIA Comparison of Quality of Life of Older Adults Living in a Licensed Dementia Housing Facility to Community Dwelling Older Adults with Dementia
Bibliographic record
Abstract
There is a momentous need for research pertaining to older adults with dementia (Hyde, Perez, & Forester, 2007).This research is especially pertinent since it is estimated that in 2026, one in five Canadians will have reached the age of 65, and the two most common mental health problems encountered by older adults are that of dementia and depression (Health Canada, 2002). In our aging population, home may be where most people wish to live out their years. For older adults that have dementia, home may not always be a realistic option (Riemer, Slaughter, Donaldson, Currie, & Eliasziw, 2004). In Canada, over half of all older adults with dementia live in institutions (Canada Study of Health and Aging, 1994). The other half of older adults with dementia most commonly live at home with help of an informal caregiver (Drunkelman & Dressel, 1994). Deciding whether to place a loved one in a nursing home is often the most difficult decision informal caregivers for older adults with dementia have to face (Kapplan, 2003). Informal caregivers are generally defined as persons who help a relative or friend, without pay, with one or more instrumental and/or basic activities of daily living (Bertrand, Fredman, & Saczynski, 2006). Informal caregivers and other members of the social support
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".