Evaluating the quality of life of long-term care residents with dementia. The Gerontologist, 45(Special Issue I
Bibliographic record
Abstract
Purpose: This study’s purpose was to better under-stand existing measures of quality of life in dementia residents of long-term care facilities. Design and Methods: We gathered data from 421 residents in 45 facilities. Analyses determined the psychometric properties of each measure, estimated the relation-ship between measures, and identified the extent to which resident characteristics predicted scores. Results: Most instruments had good to excellent dispersion and interrater reliability, and most scales had good to excellent internal consistency. Proxy measures tended to correlate best with each other, less well with observational measures, and least well with resident measures. Resident cognition and activities of daily living (ADLs) function were associ-ated with most quality-of-life measures but predicted no more than a quarter of the observed variance in any measure. Implications: Various measures and sources of data provide different perspectives on quality of life. No ‘‘gold standard’ ’ exists; so a combination of methods and sources is likely to provide the most complete picture of quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".