Changes in the harm perceptions of different types of tobacco products for youth and adults: Waves 1–5 of the population assessment of tobacco and health (PATH) study, 2013–2019
Bibliographic record
Abstract
• Tobacco harm perceptions are changing over time, along with the tobacco product marketplace and regulatory environment. • Perceptions about the relative level of harm of non-combustible tobacco products to be increasing. • These findings can also help inform the dissemination of health communication materials about the continuum of risk. Tobacco harm perceptions are associated with tobacco use for both youth and adults, but it is unknown how these harm perceptions have changed over time in a changing tobacco product landscape. Data from Waves 1–5 of the Population Assessment of Tobacco and Health (PATH) Study were analyzed to examine perceptions of harm of eight non-cigarette tobacco products compared to cigarettes. Perceptions of harm were assessed with the questions, “Is smoking/using [product] less harmful, about the same, or more harmful than smoking cigarettes?”. The share of participants who perceived non-cigarette combustible products as posing similar harm to cigarettes increased over time, while the share of participants who perceived non-combustible products as less harmful than cigarettes decreased over time. Tobacco harm perceptions are changing over time, along with the tobacco product marketplace and regulatory environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".