Developing Accessible, Pictorial Versions of Health-Related Quality-of-Life Instruments Suitable for Economic Evaluation: A Report of Preliminary Studies Conducted in Canada and the United Kingdom
Bibliographic record
Abstract
A key component of the current framework for economic evaluation is the measurement and valuation of health outcomes using generic preference-based health-related quality-of-life (HRQoL) instruments. In 2015, a research synthesis reported the absence of conceptual and empirical research regarding the appropriateness of current preference-based instruments for people with aphasia-a disorder affecting the use and understanding of language-and suggested the development and validation of an accessible, pictorial variant could be an appropriate direction for further research. This paper describes the respective rationale and development process for each of three preliminary studies that have been undertaken to develop pictorial variants of two widely used preference-based HRQoL instruments (EQ-5D-3L and EQ-5D-5L). The paper also proposes next steps for this program of research, drawing on the lessons learned from the preliminary work and the demand for a pictorial preference-based instrument in the research community. Guidance for the use of the preliminary, pictorial instruments is also provided.
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 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 | 0.092 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".