Children and youth perceive smoking messages in an unbranded advertisement from a NIKE marketing campaign: a cluster randomised controlled trial
Bibliographic record
Abstract
BACKGROUND: How youth perceive marketing messages in sports is poorly understood. We evaluated whether youth perceive that the imagery of a specific sports marketing advertisement contained smoking-related messages. METHODS: Twenty grade 7 to 11 classes (397 students) from two high schools in Montréal, Canada were recruited to participate in a cluster randomised single-blind controlled trial. Classes were randomly allocated to either a NIKE advertisement containing the phrase 'LIGHT IT UP' (n = 205) or to a neutral advertisement with smoking imagery reduced and the phrase replaced by 'GO FOR IT' (n = 192). The NIKE logo was removed from both advertisements. Students responded in class to a questionnaire asking open-ended questions about their perception of the messages in the ad. Reports relating to the appearance and text of the ad, and the product being promoted were evaluated. RESULTS: Relative to the neutral ad, more students reported that the phrase 'LIGHT IT UP' was smoking-related (37.6% vs. 0.5%) and that other parts of the ad resembled smoking-related products (50.7% vs. 10.4%). The relative risk of students reporting that the NIKE ad promoted cigarettes was 4.41 (95% confidence interval: 2.64-7.36; P < 0.001). CONCLUSIONS: The unbranded imagery of an advertisement in a specific campaign aimed at promoting NIKE hockey products appears to have contained smoking-related messages. This particular marketing campaign may have promoted smoking. This suggests that the regulation of marketing to youth may need to be more tightly controlled.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".