Valuing SF-6Dv2 Using a Discrete Choice Experiment in a General Population in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: An updated version of the Short-Form 6-Dimension (SF-6D) Classification System has been developed. This new version (SF-6Dv2) with improved consistency and dimension descriptors is now requiring the development of new utility value sets. The aim of this study was to estimate an SF-6Dv2 value set from a general population in Quebec, Canada. METHODS: ) was conducted using two designs: binary choice sets (Design 1) and best-worst choice sets (Design 2). Design 1 consisted of binary choice sets along with an associated duration, and Design 2 included Design 1 and a third scenario describing "immediate death." Various logit model specifications were employed to estimate value sets separately for Design 1 and in combination with Design 2. Heterogeneity in preferences was assessed using a mixed logit model. RESULTS: The survey was completed online by 1208 participants and 1153 were included for analysis. The model combining Design 1 and 2 data was considered as the best fitting model for estimating the final value set. It provided a value set with logical consistent coefficients and showed the lowest standard errors. Values ranged from -0.683 for the worst health state (555655) to 1 for full health (111111), with 13.01% of the values being negative. Preference values were the most affected by pain dimension and the least by vitality dimension. Preference heterogeneity existed for all the most severe levels of dimensions. CONCLUSION: This study provided the SF-6Dv2 value set for use in Quebec, Canada. The recommended value set is the anchored consistent model combining data from Design 1 and 2 using a conditional logit.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".