The impact of social and psychological consequences of disease on judgments of disease severity: An experimental study
Bibliographic record
Abstract
BACKGROUND: The Global Burden of Disease (GBD) project systematically assesses mortality, healthy life expectancy, and disability across 195 countries and territories, using the disability-adjusted life year (DALY). Disability weights in the DALY are based upon surveys that ask users to rate health states based on lay descriptions. We conducted an experimental study to examine whether the inclusion or removal of psychological, social, or familial implications from a health state description might affect individual judgments about disease severity, and thus relative disability weights. METHODS: We designed a survey consisting of 36 paired descriptions in which information about plausible psychological, social, or familial implications of a health condition was either present or absent. Using a Web-based platform, we recruited 1,592 participants, who were assigned to one of two experimental groups, each of which were asked to assign a value to the health state description from 0 to 100 using a slider, with 0 as the "worst possible health" and 100 as the "best possible health." We tested five hypotheses: (1) the inclusion of psychological, social, or familial consequences in health state descriptions will reduce the average rating of a health state; (2) the effect will be stronger for diseases with lower disability weights (i.e., less severe diseases); (3) the effect will vary across the type of additional information added to the health state description; (4) the impact of adding information on familial consequences will be stronger for female than male; (5) the effect of additional consequences on ratings of health state descriptions will not differ by levels of completed education and age. RESULTS: On average, adding social, psychological, or familial consequences to the health state description lowered individual ratings of that description by 0.78 points. The impact of adding information had a stronger impact on ratings of the least severe conditions, reducing average ratings in this category by 1.67 points. Addition of information about child-rearing had the strongest impact, reducing average ratings by 2.09 points. We found little evidence that the effect of adding information on ratings of health descriptions varied by gender, education, or age. CONCLUSIONS: Including information about health states not directly related to major functional consequences or symptoms, particularly with respect to child-rearing and specifically for descriptions of less severe conditions, can lead to lower ratings of health. However, this impact was not consistent across all conditions or types of information, and was most pronounced for inclusion of information about child-rearing, and among the least severe conditions.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".