Validation of disability categories derived from Health Utilities Index Mark 3 scores.
Bibliographic record
Abstract
OBJECTIVES: To establish empirical evidence for the validity of the following disability categories derived from Health Utilities Index Mark III (HUI3) global utility scores: none (1.00), mild (0.89 to 0.99), moderate (0.70 to 0.88), and severe (less than 0.70). DATA AND METHODS: Data from the 2005 Canadian Community Health Survey (cycle 3.1) were analyzed. Frequency distributions, stratum-specific likelihood ratios, and multinomial regression were used to examine the relationship between health indicators and the HUI3 disability categories. RESULTS: People reporting chronic conditions, activity restrictions, and fair/poor self-rated health (general and mental) were more likely to be in the moderate and severe disability categories. Those having more positive outcomes on the health indicators tended to fall into the mild and no disability groups. The stratum-specific likelihood ratios increased monotonically with the severity of disability level. Compared to those with positive health status characteristics, those with negative health status characteristics had the highest odds of falling in the severe rather than the non-disabled category. INTERPRETATION: This study makes an initial contribution to the evidence base for the validity of the proposed HUI3 disability categories. The categories were well-supported empirically and are likely to be useful for assessing disability levels.
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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 | 0.035 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".