The Rutgers Omnibus Study: Protocol for Quarterly Web-Based Surveys to Promote Rapid Tobacco Research
Bibliographic record
Abstract
BACKGROUND: Rapid and flexible data collection efforts are necessary for effective monitoring and research on tobacco and nicotine product use in a constantly evolving marketplace. The Rutgers Omnibus Survey (1) provides timely data on awareness and use of new and emerging tobacco products among adults in a rapid manner, (2) provides a platform for measurement experiments to help develop and refine measures of tobacco use that reflect the current marketplace, and (3) generates pilot data for grant applications and scientific manuscripts. OBJECTIVE: This study aims to document the first 2 years of the Rutgers Omnibus Study through the reporting of methodology, fielding summaries, and sample characteristics. METHODS: Launched in February 2022 and fielded quarterly thereafter, we survey convenience samples of 2000 to 3000 US adults aged 18-45 years recruited from Amazon Mechanical Turk (MTurk) using the MTurk Toolkit by CloudResearch. The questionnaire includes core and rotating modules and is designed to take approximately 10 minutes to complete through Qualtrics. The fielding duration is approximately 10 days per wave. Each wave includes both unique and repeating participants, and responses can be linked across waves by an anonymous ID. RESULTS: Sample sizes ranged from 2082 (wave 8, December 2023) to 2989 (wave 1, February 2022), and the 8-wave longitudinal dataset included 10,334 participants, of whom 2477 had 3 or more data points. The cost per complete at each wave was low, ranging from US $2.46 to US $3.27 across waves. Key demographics were consistent across waves and similar to that of the general population, while tobacco product trial and past-30-day use were generally higher. CONCLUSIONS: The Rutgers Omnibus Study is a quarterly survey that is effective for rapidly assessing the use of emerging tobacco and nicotine products and can also be leveraged to conduct survey experiments, generate pilot data, and address both cross-sectional and longitudinal research questions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/58203.
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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 | 0.052 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.113 | 0.038 |
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".