MétaCan
Menu
Back to cohort
Record W1990062469 · doi:10.2190/9px0-nbg1-0ala-g5yh

Comparison of Current U.S. and Canadian Cigarette Pack Warnings

2004· article· en· W1990062469 on OpenAlexaboutno aff
Ashish D. Nimbarte, Fereydoun Aghazadeh, Craig Harvey

Bibliographic record

VenueInternational Quarterly of Community Health Education · 2004
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsTobacco controlEnvironmental healthMedicineTobacco productCigarette smokingAdvertisingDemographyBusinessPublic healthNursing

Abstract

fetched live from OpenAlex

Cigarette smoking is the single most preventable cause of death in the United States. From 1985 to date, one of four mandatory cigarette warnings proposed by the Comprehensive Smoking Education Act of 1984 has been displayed on cigarette packages. In addition to cigarette warnings, states like California, Massachusetts, Arizona, Oregon and Maine have implemented "Tobacco Control Programs" (TCP) to reduce the overall number of smokers. However, the decline in the rate of smoking is not occurring fast enough to meet the national health objective by 2010. The present U.S. cigarette warnings are verbal in form and provide information, which is inadequate but appropriate to make it legally adequate. On the other hand, warnings in other countries such as Canada and Brazil are more descriptive and specific and are accompanied by vivid and sometimes gruesome pictures. In the present study, six pictorial Canadian labels and four U.S. verbal labels were analyzed for potential effectiveness among eighty subjects using a survey questionnaire. The survey findings are compared with recent Canadian smoking data. It is concluded that placing pictorial labels on cigarette packages in the U.S. will allow the product to carry warnings that potentially provide better results than current verbal messages and less TCP funds will need to be used.

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 score0.433
Threshold uncertainty score0.995

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 head0.075
GPT teacher head0.441
Teacher spread0.366 · 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

Citations14
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueInternational Quarterly of Community Health EducationSame topicSmoking Behavior and CessationFrench-language works237,207