Developing New Methods and Questions for Improving Response and Measurement on Sensitive Questions on the National Health Interview Survey
Bibliographic record
Abstract
The National Center for Health Statistics (NCHS) conducted a study during the second quarter of the 2006 data collection year of the National Health Interview Survey (NHIS) to test alternative income questions, new wealth questions, and respondent willingness to provide partial Social Security numbers. The field test took advantage of the oversampling feature of the NHIS sample design, which specifies that households that are designated for oversampling (15 % of the 45,000 households) and that do not have at least one black, Hispanic, or Asian member are screened out, i.e., excluded from the sample. For this study, the usually-excluded households were interviewed and treated as an independent sample. We asked those respondents to answer the test questions along with some of the standard NHIS questions. The purpose of this study is to test methods for asking sensitive questions and determine the feasibility of using data from “screened out” households in future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.310 | 0.181 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".