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Record W2542718440 · doi:10.1093/ntr/ntw298

The Great Recession, Adolescent Smoking, and Smoking Inequalities: What Role Does Youth Unemployment Play in 24 European Countries?

2016· article· en· W2542718440 on OpenAlexaff
Katharina Rathmann, Timo‐Kolja Pförtner, Frank J. Elgar, Klaus Hurrelmann, Michael Richter

Bibliographic record

VenueNicotine & Tobacco Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsUnemploymentYouth unemploymentRecessionYouth smokingSocioeconomic statusInequalityDemographySmoking prevalenceYoung adultMedicinePsychologyDemographic economicsTobacco controlEnvironmental healthPublic healthEconomicsGerontologyPopulationEconomic growthSociology

Abstract

fetched live from OpenAlex

INTRODUCTION: Conflicting evidence has been reported on smoking behavior among adults during times of economic downturn. No study has yet investigated young people's smoking and inequalities in smoking during economic recessions. This study examines the association between country-level youth unemployment due to the economic recession and adolescent smoking and smoking inequalities in Europe. METHODS: The WHO collaborative "Health Behaviour in School-aged Children" study in 2009/2010 included 15-year-old adolescents from 24 European countries (N = 43 093). Socioeconomic position (SEP) was measured by the Family Affluence Scale. Logistic multilevel models were conducted. The absolute rate of youth unemployment in 2010 (during the recession) and the relative change rate in youth unemployment (2005/2006-2009/2010) were regressed on smoking and SEP inequalities in smoking in 2010, respectively. RESULTS: Youth unemployment rates were not significantly associated with overall smoking in adolescents. A higher absolute youth unemployment rate in 2010 related to lower likelihoods of smoking among middle (OR: 0.99; 95% CI: 0.98-0.99) and low affluent adolescents (OR: 0.99; 95% CI: 0.98-0.99) compared to high affluent adolescents. In contrast, an increase in youth unemployment (2005/2006-2009/2010) was not associated with overall likelihoods of smoking and inequalities in smoking. CONCLUSIONS: Our findings indicate that an increase in youth unemployment was not related to smoking and smoking inequalities. However, higher absolute levels of youth unemployment are related to lower likelihoods of smoking in lower SEP adolescents. Thus, smoking among vulnerable groups is more linked to the overall insecure circumstances and the affordability of cigarettes rather than to the economic recession itself. IMPLICATIONS: Economic recessions have often led to increases in adult and youth unemployment rates. Conflicting evidence has been reported on smoking behavior among adults during times of economic downturn. This study examines for the first time the impact of the economic recession on young people's smoking and socioeconomic inequalities in smoking. Findings highlight that rather than an increase in youth unemployment, the overall country-level youth unemployment rate is related to young people's tobacco use and particularly to lower odds in smoking among less affluent adolescents across Europe, a finding which is likely to be linked to the affordability of tobacco use.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.115
GPT teacher head0.421
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2016
Admission routes1
Has abstractyes

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