MétaCan
Menu
Back to cohort

Poverty and smoking

2000· book-chapter· en· W2301996658 on OpenAlexaboutno aff
Martin Bobák, Prabhat Jha, S Nguyen

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPovertySmoking prevalenceConsumption (sociology)Social classDemographyGeographyTobacco controlLow and middle income countriesTobacco useSmokeEnvironmental healthMedicineDeveloping countryPolitical sciencePublic healthPopulationEconomic growthSociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

Abstract This chapter examines the association between poverty and tobacco use. It provides a comprehensive review of the data on smoking prevalence and consumption levels in different sodo-economic groups, both within individual countries and internationally. It finds that smoking is more common among poor men (variously defined by income, education, occupation, or sodal class) than rich men in nearly aD countries. In high income countries, the social gradients of smoking are clearly established for men: smoking has been widespread for several decades, and smoking-attributable mortality can be measured reliably. Analyses of smoking-attributable mortality in middle age (defined as ages 35–69) in Canada, England and Wales, Poland, and the United States reveal that smoking is responsible for most of the excess mortality of poor men in these countries. For women, the situation is more variable, partly reflecting the more recent onset of mass smoking by women in certain parts of the world. Why poor people smoke more remains a complex question that requires further research.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.003

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 head0.035
GPT teacher head0.311
Teacher spread0.277 · 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

Citations107
Published2000
Admission routes1
Has abstractyes

Explore more

Same topicHealth disparities and outcomesFrench-language works237,207