The Determinants of Quality of Life of Nursing Home Residents with Young-Onset Dementia and the Differences between Dementia Subtypes
Bibliographic record
Abstract
AIMS: The aims of this study are to (1) explore the determinants of quality of life (QoL) in nursing home residents with young-onset dementia (YOD), (2) investigate whether there are differences between dementia subtypes (Alzheimer dementia, vascular/mixed dementia, frontotemporal dementia, other) regarding these determinants, and (3) compare QoL profiles of YOD nursing home residents across dementia subtypes. METHODS: This cross-sectional study included 207 nursing home residents. Multilevel modeling was used to determine the relationships between QoL and neuropsychiatric symptoms (NPS), dementia severity, psychotropic drug use (PDU), dementia subtype, age, and gender. Additional multilevel models were used to compare aspects of QoL between dementia subtypes. RESULTS: Residents' QoL was negatively associated with advanced dementia, PDU, and NPS. In general, the relationships between the determinants and QoL were similar across the dementia subtypes. Aspects of QoL differed by dementia subtype. Residents with frontotemporal dementia showed less negative emotions, accepted more help and experienced better quality of relationships with professional caregivers, had a more positive self-image, felt more comfortable in the nursing home environment, and experienced lower quality of social relationships. CONCLUSIONS: Considering the high rates of NPS and PDU in YOD residents and their negative associations with QoL, we recommend emphasizing services to manage and reduce NPS and PDU in nursing home residents with YOD. Furthermore, our findings suggest accounting for differences in aspects of QoL by dementia subtype to address specific needs and thereby improve QoL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".