What were they thinking when providing preference measurements for generic health states? The evidence for HUI3
Bibliographic record
Abstract
BACKGROUND: Multi-attribute generic preference-based measures of health-related quality of life are used as comprehensive outcome measures. Typically preferences for health states defined by these systems are elicited from a representative sample of the general population. An important element in that elicitation process is the information that respondents were instructed to consider in providing their responses. METHODS: A random sample of community-dwelling respondents in Canada was surveyed in face-to-face interviews. Respondents provided preference scores for selected Health Utilities Index Mark 3 (HUI3) health states. Respondents also answered questions about the most important attributes and the importance of various impacts of the health states in providing their preference scores. RESULTS: Fifty per cent of respondents reported that they focussed on two, and 21% on three, attributes of the eight HUI3 attributes. Each of the eight attributes was identified as important; pain (49%), vision (37%), cognition (34%), emotion (28%), and ambulation (28%) were the most important. The null hypothesis that all of the attributes were equally important was rejected (p < 0.001). With respect to the impacts, 89% of respondents indicated that the ability to take care of oneself was quite or very important; similarly 76% reported the same for impact on family life, 69% for impact on the happiness of others, 61% for the impact on their ability to work, and 42% for the impact on their leisure activities. The null hypothesis that all of the impacts were equally important was rejected (p < 0.001). CONCLUSIONS: In providing preference scores for HUI3 health states, respondents thoughtfully examined the implications of the health states for their ability to live, work, socialize, and function.
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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 | 0.183 | 0.351 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".