Estimation of a Canadian preference-based scoring algorithm for the Veterans RAND 12-Item Health Survey: a population survey using a discrete-choice experiment
Bibliographic record
Abstract
BACKGROUND: The Veterans RAND 12-Item Health Survey (VR-12) is a generic patient-reported outcome measure derived from the widely used 36- and 12-item Short Form Health Surveys. We aimed to estimate a Canadian preference-based scoring algorithm for the VR-12, enabling the derivation of health utility values for generating quality-adjusted life years (QALYs). METHODS: We conducted a discrete-choice experiment in a sample of the Canadian population in January and February 2019. Participants - recruited from a consumer research panel - completed an online survey, in English or French, that included 11 discrete-choice questions, each comprising 2 health profiles. We defined the health profiles using 8 VR-12 items and a duration attribute. Using conditional logit regressions, where each level of the respective VR-12 items was interacted with duration, we applied the coefficients to estimate health utility values interpretable on a scale of 0 (dead) to 1 (full health). Negative values reflect states considered worse than dead. RESULTS: A total of 3380 individuals completed the survey. Of these, 1688 (49.9%) were females, and 3101 (91.7%) completed the English version of the survey. Across all models, "feel downhearted and blue all of the time" and "pain interferes with your normal work extremely" were associated with the largest decrements in health utility. Excluding the 685 respondents (20.3%) who provided inconsistent responses had a negligible effect on the results. The recommended model, weighted to match population demographics, had health utility values ranging from -0.589 to 1.000. INTERPRETATION: Health utility values that reflect the preferences of the Canadian population can now be derived from responses to the VR-12. These values can be used to generate QALYs in future analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".