Exposure to Electronic Cigarette Television Advertisements Among Youth and Young Adults
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE Currently, the US Food and Drug Administration does not regulate electronic cigarette (e-cigarette) marketing unless it is advertised as a smoking cessation aid. To date, the extent to which youth and young adults are exposed to e-cigarette television advertisements is unknown. The objective of this study was to analyze trends in youth and young adult exposure to e-cigarette television advertisements in the United States. METHODS Nielsen data on television household audiences’ exposure to e-cigarette advertising across US markets were examined by calendar quarter, year, and sponsor. RESULTS Youth exposure to television e-cigarette advertisements, measured by target rating points, increased 256% from 2011 to 2013. Young adult exposure increased 321% over the same period. More than 76% of all youth e-cigarette advertising exposure occurred on cable networks and was driven primarily by an advertising campaign for 1 e-cigarette brand. CONCLUSIONS E-cigarette companies currently advertise their products to a broad audience that includes 24 million youth. The dramatic increase in youth and young adult television exposure between 2011 and 2013 was driven primarily by a large advertising campaign on national cable networks. In the absence of evidencebased public health messaging, the current e-cigarette television advertising may be promoting beliefs and behaviors that pose harm to the public health. If current trends in e-cigarette television advertising continue, awareness and use of e-cigarettes are likely to increase among youth and young adults.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".