Introducing Educational Attainment to the Poststratification Adjustment in the National Survey on Drug Use and Health
Bibliographic record
Abstract
The National Survey on Drug Use and Health (NSDUH) provides national estimates of substance use and mental health among the civilian, noninstitutionalized population aged 12 or older in the United States. Since Quarter 4 of 2020, multimode (web and in-person) data collection has been employed in NSDUH. Adult web respondents had higher levels of educational attainment than adult in-person respondents, and educational attainment is often correlated with survey outcomes in NSDUH. To correct the imbalance of the educational attainment distributions across survey modes, educational attainment was added as a covariate to the poststratification adjustment in the 2020, 2021, and 2022 NSDUH weighting. Educational attainment proportions calculated from 1-year American Community Survey (ACS) data were used to derive control totals for the main effect and two-way interactions of educational attainment by demographic variables and by state. Two approaches for calculating educational attainment proportions, marginal distribution and cell distribution, were compared for accuracy across domains and summation of subdomains. The impact of excluding the institutionalized population and the active-duty military population from the 1-year ACS data was also investigated and is discussed.
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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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".