Health-related quality of life measure based
Bibliographic record
Abstract
Objectives: To introduce a health-related quality of life measure for home care and institutional long-term care settings based on the Minimum Data Set (MDS) and the Health Utilities Index Mark 2 (HUI2). Methods: Health attributes of Health Related Quality of Life (HRQOL) were identified, and suitable constructs were determined. Items from the MDS were mapped to the HUI2. Scores for the Minimum Data Set Health Status Index (MDS-HSI) were calculated using the HUI2 scoring function. Measurement properties are examined and reported. HRQOL scores were compared across study populations and to an external reference population. Random samples were drawn from long-term care clients in private households (n = 377), supportive housing apartments (n = 80), two residential care facilities (n = 166), and a chronic care hospital (n = 274) in Ontario, Canada. All sampled residents were assessed for health-related items using the MDS. Results: The MDS-HSI results provide preliminary evidence of good validity. Institutional populations had lower overall HRQOL scores than community populations. Comparisons to existing Canadian na-tional data support construct validity. Conclusions: The MDS-HSI provides a summary outcome measure and an indicator of health status in the six supporting attributes. Longitudinal research is required to assess the sensitivity of the measure to changes over time. Further research is also required to establish the consistency between the preference weights used in this application of the HUI2 and those that would be derived from a frail elderly population.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".