YouTube: a promotional vehicle for little cigars and cigarillos?
Bibliographic record
Abstract
OBJECTIVE: As the third most popular website in the world, messages embedded in the video-sharing site, YouTube, have the potential to influence tobacco-related attitudes, beliefs and behaviours. Despite the growing number of videos depicting little cigars/cigarillos (LCCs), there has been no examination of the portrayal of these products on YouTube. METHODS: Researchers identified up to the top 20 search results on YouTube by relevance and view count for the following search terms: 'little cigars', 'cigarillos', 'Black and Mild', 'Swisher Sweets', 'White owl', 'Garcia y Vega', and 'Winchester'. Reviewers rated whether videos were 'pro', 'anti' or 'neutral' to the use of LCCs, and documented statistics on the reach and viewer demographics. Several main themes around LCCs were noted, as was video quality (amateur vs professional) and demographics of video participants. RESULTS: Of the 196 videos retrieved, only 56 were unique, eligible videos. The majority of these (n=43) were 'pro' LCCs, 11 were 'neutral', and only two were 'anti' LCCs. Videos were primarily viewed by males in the USA and Canada and most were amateur. Common themes included where to purchase LCCs, their candy flavours, and that they are cheap or cheaper than cigarettes, and 'smooth'. CONCLUSIONS: The vast majority of information on YouTube about LCCs promotes their use. It is critical to monitor content on LCCs posted on YouTube, and develop appropriate health messages to counter pro-LCC content, and appropriately inform potential consumers of the harms associated with their use.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".