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Record W2076363597 · doi:10.1080/02813430802588675

When do adolescents become smokers?

2008· article· en· W2076363597 on OpenAlexaff
Ingrid Edvardsson, Lena Lendahls, Anders Håkansson

Bibliographic record

VenueScandinavian Journal of Primary Health Care · 2008
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsCentre for Family Medicine
Fundersnot available
KeywordsSnusMedicineSnuffDemographyTobacco useSmokeless tobaccoEnvironmental healthPediatricsPopulation

Abstract

fetched live from OpenAlex

OBJECTIVE: To follow the development of a class of pupils' tobacco habits for seven years, and to study differences in tobacco use between girls and boys. SETTING: Kronoberg County in southern Sweden. SUBJECTS: All the approximately 2000 pupils were followed from approximately age 12 to approximately age 18. DESIGN: Yearly cross-sectional surveys from 1994 to 2000. Each year, the pupils filled in an established tobacco questionnaire. They did it anonymously in the classroom. MAIN OUTCOME MEASURES: Percentage of smokers, number of cigarettes smoked per day, and percentage of pupils using "snus", the Swedish variety of oral moist snuff. RESULTS: From grade 6 of compulsory school to grade 12 of upper secondary school, the proportion of daily smokers rose, from 0.2% to 22% for girls and from 0.5% to 14% for boys. Among both genders, the increase occurred mainly between grades 7 and 10, and from grade 10 onwards the daily smokers were the largest group of smokers. Starting from grade 9, boys had higher total tobacco consumption than girls, as a result of their increased use of "snus", and at the end of the study 39% of the boys used tobacco compared with 34% of the girls. CONCLUSION: Studying young people's tobacco habits over time gives an understanding of when preventive measures should be implemented. In order for these to influence attitudes, they should be put in place well before tobacco is introduced.

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 score0.027
Threshold uncertainty score0.498

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 head0.029
GPT teacher head0.310
Teacher spread0.281 · 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

Citations18
Published2008
Admission routes1
Has abstractyes

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