Validation of a Health-Related Quality of Life Measure based on the Minimum Data Set by Mapping and Regression
Bibliographic record
Abstract
This study is the first to directly compare the Minimum Data Set- Health Status Index (MDS-HSI) based prediction of health-related quality of life (HRQOL) using the Resident Assessment Instrument- Minimum Data Set (RAI-MDS) with the actual resident-ratings of HRQOL based on the Health Utilities Index-2 (HUI2). This study involved secondary analysis of HUI2 survey response data from long-term care residents in Ontario. At the individual level, there was poor correlation between MDS-HSI and HUI2 multi-attribute and single health attribute scores. The Self-care attribute was the most discrepant health attribute and had the largest impact on the poor overall ICC between the MDS-HSI and the HUI2 scores. At the group level, the MDS-HSI and HUI2 findings showed mixed results. The results suggest that the source of HRQOL information is an important factors to consider when conducting HRQOL research in long-term care settings.
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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 | 0.027 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".