Mode of Administration Is Important in US National Estimates of Health-Related Quality of Life
Bibliographic record
Abstract
BACKGROUND: It is unknown if different national surveys that vary in mode of administration yield similar national averages for health-related quality of life (HRQoL). PURPOSE: Examine HRQoL scores from 4 surveys representative of the noninstitutionalized US adult population for patterns related to age, gender, and mode of administration. METHODS: We use data from the Joint Canada/United States Survey of Health (JCUSH; telephone survey), 2002 Medical Expenditure Panel Survey (MEPS; mail survey), National Health Measurement Study (NHMS; telephone survey), and US Valuation of the EuroQol EQ-5D Health States Survey (USVEQ; self-administered with interviewer present). We compare estimates from the EQ-5D, Visual Analog Scale, Health Utilities Index Mark 3, and general self-rated health stratified by age and gender. Scores were also regressed on age and gender within each survey and in a pooled analysis. RESULTS: We used 4939 subjects from JCUSH, 23,006 from MEPS, 3844 from NHMS, and 3878 from USVEQ. The majority of age and gender strata had instrument completion rates above 85%. Age- and gender-stratified estimates of HRQoL scores tended to be consistent when mode of administration (self- or interviewer-administered) was the same. Telephone administration yielded more positive HRQoL estimates than self-administration in older age groups. Older age groups and females reported lower HRQoL than younger age groups and males regardless of mode of administration. CONCLUSIONS: When choosing survey-collected HRQoL scores for comparative purposes, analysts need to take mode of administration into account.
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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 | 0.035 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".