Differences Between Individual and Societal Health State Valuations
Bibliographic record
Abstract
OBJECTIVE: The concept of "adaptation" has been proposed to account for differences between individual and societal valuations of specific health states in patients with chronic diseases. Little is known about psychological indices of adaptational capacity, which may predict differences in individual and societal valuations of health states. We investigated whether such differences were partially explained by personality traits in chronic disease patients. RESEARCH DESIGN: Analysis of baseline data of randomized controlled trial. SUBJECTS: Three hundred seventy patients with chronic disease. MEASURES: The NEO-five factor inventory measure of personality, EuroQoL-5D (EQ-5D) societal-based, and the EQ visual analogue scale individually-based measures of health valuation. RESULTS: Regression analyses modeled Dev, a measure of difference between the EQ-Visual Analogue Scale and EQ-5D, as a function of personality traits, sociodemographic factors, and chronic diseases. Individual valuations were significantly and clinically higher than societal valuations among patients in the second and third quartile of conscientiousness (Dev = 0.08, P = 0.01); among covariates, only depression (Dev = -0.04, P = 0.046) was also associated with Dev. CONCLUSION: Compared with societal valuations of a given health state, persons at higher quartiles of conscientiousness report less disutility associated with poor health. The effect is roughly twice that of some estimates of minimally important clinical differences on the EQ-5D and of depression. Although useful at the aggregate level, societal preference measures may systematically undervalue the health states of more conscientious individuals. Future work should examine the impact this has on individual patient outcome evaluation in clinical studies.
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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".