Assessing quality of life of nursing home residents with dementia: feasibility and limitations in patients with severe cognitive impairment
Bibliographic record
Abstract
BACKGROUND: The Quality of Life-Alzheimer's Disease Scale (QOL-AD) is a reliable and valid self-report measure for assessing quality of life (QoL) in people with dementia in long-term care settings, but little is known yet about the number of patients with severe cognitive impairment who are able to complete this measure, and the characteristics of those unable to do so. The aim of the study is to advance knowledge of these issues. METHODS: Data on residents with dementia were collected from 11 nursing homes. The QOL-AD residential version was directly applied to residents with dementia diagnosis and Mini-Mental State Examination scores under 27, randomly selected in each center. Residents' QoL was further assessed from the perspective of some close relative and some staff member. Altogether, 102 data sets from residents, 184 from relatives, and 197 from staff members were collected. An analysis of the characteristics of completers versus non-completers regarding levels of cognitive impairment was carried out. RESULTS: People with dementia in long-term care are able to report their QoL. The QOL-AD completion rate decreases as the cognitive impairment level increases; non-completion is associated with greater overall impairment. About 30% of residents with severe cognitive impairment could self-report on their QoL with acceptable reliability. CONCLUSIONS: QoL self-rating should be the first-line option when assessing residents with severe cognitive impairment. For those that are not able to complete self-report measures, proxies' report could be an alternative, although the development of other assessment procedures (e.g. observational) should be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".