Exposure to digital vape marketing among young people in Aotearoa New Zealand
Bibliographic record
Abstract
AIMS: Little is known about the exposure of young people in Aotearoa New Zealand to marketing of vape products on social media. This study investigated vaping behaviour and the extent of vape marketing exposure and engagement that young people (14-20 years) report on social media and examined differences across socio-demographic groups. METHODS: An online survey was conducted with 3,698 participants aged between 14-20 years (M=17.1; SD=1.8). A range of genders (55.7% females, 38.3% males and 6% another gender), ethnicities (25.6% Māori, 46.7% Pākehā or NZ European, 6.5% Pasifika and 21.2% another ethnicity) and social classes took part. RESULTS: Half (50.8%; n=1,110) of the respondents (N=2,185) reported that they had vaped at least once; vaping history was positively related to exposure to and engagement with digital vape marketing. Half (50.3%; n=1,119) of the respondents (N=2,224) reported seeing vape marketing on at least one social media platform. Binary logistic regressions showed that younger respondents were more likely to report seeing vape marketing than older respondents, and Māori and Pasifika more likely than other ethnicities. Over a quarter (26%; n=563) of respondents (N=2,148) reported engaging with vape marketing online, with Māori and Pasifika respondents more likely to engage than other ethnicity groups, and similarly for respondents of lower compared to higher socio-economic status. No interaction effects were found. CONCLUSIONS: Many young people, including a subset under the legal age for purchase, reported seeing vape product marketing on social media platforms. Patterns of exposure to vape product marketing on social media mirror the inequitable marketing exposure of harmful commodities in physical environments. Improved transparency and regulation of social media marketing is required.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".