Resident health-related quality of life in Swiss nursing homes
Bibliographic record
Abstract
BACKGROUND: Health-related quality of life (HRQOL) levels and their determinants in those living in nursing homes are unclear. The aim of this study was to investigate different HRQOL domains as a function of the degree of cognitive impairment and to explore associations between them and possible determinants of HRQOL. METHOD: Five HRQOL domains using the Minimum Data Set - Health Status Index (MDS-HSI) were investigated in a large sample of nursing home residents depending on cognitive performance levels derived from the Cognitive Performance Scale. Large effect size associations between clinical variables and the different HRQOL domains were looked for. RESULTS: HRQOL domains are impaired to variable degrees but with similar profiles depending on the cognitive performance level. Basic activities of daily living are a major factor associated with some but not all HRQOL domains and vary little with the degree of cognitive impairment. LIMITATIONS: This study is limited by the general difficulties related to measuring HRQOL in patients with cognitive impairment and the reduced number of variables considered among those potentially influencing HRQOL. CONCLUSION: HRQOL dimensions are not all linearly associated with increasing cognitive impairment in NH patients. Longitudinal studies are required to determine how the different HRQOL domains evolve over time in NH residents.
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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 | 0.002 |
| 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) | 0.002 | 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".