Health literacy and logical inconsistencies in valuations of hypothetical health states: results from the Canadian EQ-5D-5L valuation study.
Bibliographic record
Abstract
OBJECTIVES: To examine the association of health literacy with logical inconsistencies in time trade-off valuations of hypothetical health states described by the EQ-5D-5L classification system. METHODS: Data from the EQ-5D-5L Canadian Valuation study were used. Health literacy was assessed using the Brief Health Literacy Screen. A health state valuation was considered logically inconsistent if a respondent gave the same or lower value for a very mild health state compared to the value given to 55555, or gave the same or lower value for a very mild health state compared to value assigned to the majority of the health states that are dominated by the very mild health state. RESULTS: Average age of respondents (N = 1209) was 48 (SD = 17) years, 45% were male, 7% reported inadequate health literacy, and 11% had a logical inconsistency. In adjusted analysis, participants with inadequate health literacy were 2.2 (95%CI: 1.2, 4.0; p = 0.014) times more likely to provide an inconsistent valuation compared to those with adequate health literacy. More specifically, those who had problems in "understanding written information" and "reading health information" were more likely to have a logical inconsistency compared to those who did not. However, lacking "confidence in completing medical forms" was not associated with logical inconsistencies. CONCLUSIONS: Health literacy was associated with logical inconsistencies in valuations of hypothetical health states described by the EQ-5D-5L classification system. Valuations studies should consider assessing health literacy, and explore better ways to introduce the valuation tasks or use simpler approaches of health preferences elicitation for individuals with inadequate health literacy.
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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 | 0.013 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".