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Record W2154698062 · doi:10.1136/tc.9.suppl_2.ii18

Anti-smoking advertising campaigns targeting youth: case studies from USA and Canada

2000· article· en· W2154698062 on OpenAlexaboutno aff
Cornelia Pechmann

Bibliographic record

VenueTobacco Control · 2000
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
FundersTobacco-Related Disease Research Program
KeywordsAdvertisingYouth smokingAdvertising campaignSmoking preventionPolitical scienceBusinessEnvironmental healthSmoking cessationMedicineTobacco controlPublic health

Abstract

fetched live from OpenAlex

OBJECTIVE: To assist in planning anti-smoking advertising that targets youth. Using five US state campaigns, one US research study, and a Canadian initiative as exemplars, an attempt is made to explain why certain advertising campaigns have been more cost effective than others in terms of reducing adolescent smoking prevalence. Several factors which prior research and theory suggest may be important to cost effectiveness are examined. Specifically, three variables pertaining to the advertising message (content, consistency, and clarity) and two variables related to the advertising execution or style (age of spokesperson and depiction of smoking behaviour) are studied. DESIGN: A case study approach has been combined with supplemental data collection and analysis. To assess campaign effects, published articles and surveys of adolescent smoking prevalence in campaign versus control (non-campaign) locations were utilised. Adolescent subjects provided supplemental data on the advertising message variables. Trained adults content analysed each advertisement to assess the executional variables. SUBJECTS: A total of 1128 seventh grade (age 12-13 years) and 10th grade (age 15-16 years) students participated in the supplemental data collection effort. RESULTS: An anti-smoking advertising campaign initiated by Vermont researchers was found to be the most cost effective in that it significantly reduced adolescent smoking prevalence at a low per capita cost. Next in order of cost effectiveness were California, Massachusetts, and Florida because behavioural outcomes were inconsistent across time and/or grades. California was ranked higher than the other two because it spent less per capita. Minnesota and Canada were ineffective at reducing adolescent smoking prevalence, and no comparison outcome data were available for Arizona. Four factors were found to be associated with increased cost effectiveness: (1) a greater use of message content that prior research suggests is efficacious with youth; (2) a more concentrated use of a single efficacious message; (3) an avoidance of unclear messages; and (4) an increased use of youthful spokespeople that adolescents could more readily identify with. No indication was found that depictions of smoking undermined campaign effectiveness by inadvertently implying that smoking was prevalent. CONCLUSIONS: The highly cost effective Vermont campaign can be used as a model for future efforts. It is estimated that 79% of the Vermont advertisements conveyed efficacious messages, 58% concentrated on a single efficacious message, 70% showed youthful spokespeople, and only 4% contained unclear messages. The results suggest that, in the less effective campaigns, as few as 25% of the advertisements contained messages that prior research indicates should be efficacious with youth, as few as 10% of the advertisements focused on one efficacious message, and up to 32% of the advertisements lacked clearcut messages.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.273
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations81
Published2000
Admission routes1
Has abstractyes

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