Manipulating the 5 Dimensions of the EuroQol Instrument: The Effects on Self-Reporting Actual Health and Valuing Hypothetical Health States
Bibliographic record
Abstract
Background. The EQ-5D instrument has 5 dimensions. This article reports on the effects of manipulating a) the order in which the 5 dimensions are presented (appearing first v. last), b) splitting of the composite dimensions (“pain or discomfort” and “anxiety or depression”), and c) removing or “bolting off” 1 of the 5 EQ-5D dimensions at a time. The effects were examined in 2 contexts: 1) self-reporting health and 2) health state valuations. Methods. Three different types of discrete choice experiments (DCE) including a duration attribute were designed. An online survey with 12 subtypes, each with 10 DCE tasks, was designed and completed by 2494 members of the UK general public. Results. Of the 3 manipulations in the self-reporting context, only b) splitting anxiety or depression had a significant effect. In the health state valuation context, b) splitting level 5 pain or discomfort (relative to pain) and splitting level 5 anxiety or depression (relative to anxiety) had significant effects as did c) bolting off dimensions. Conclusions. We find that the values given to certain health dimensions are sensitive to the way in which it is described and the other health dimensions presented. Of particular interest is the effect of splitting composite dimensions: a given EQ-5D(-5L) profile may mean different things depending on whether the profile is used to self-report one’s health or to value hypothetical states, so that the health state values of EQ-5D(-5L) in population tariffs may not correspond to the states that patients self-report themselves in.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".