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Implementation of smokefree workplaces: challenges in Latin America

2010· article· en· W2017447780 on OpenAlexaff
Gillian Griffith, Antonella Cardone, Catherine L. Jo, Ami Valdemoro, Ernesto M Sebrié

Bibliographic record

VenueSalud Pública de México · 2010
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsTrinity College
FundersFlight Attendant Medical Research InstituteAmerican Cancer Society
KeywordsLatin AmericansLegislationTobacco controlPolitical scienceTobacco industrySmokeBusinessEconomic growthPublic administrationLawEnvironmental healthPublic healthEngineeringMedicineEconomics

Abstract

fetched live from OpenAlex

Latin America is at the forefront of global progress in smoke free workplaces. Comprehensive smoke free laws have been implemented in four countries, and in many cities, states and provinces. More than 130 million people in Latin America are now protected from secondhand tobacco smoke. Nevertheless, a survey of tobacco control advocates and governments in Latin America found several challenges to progress in smoke free workplaces: the need for voluntary workplace programs where there is no smoke free legislation; weak legislation or lack of comprehensive national smoke free laws; tobacco industry attempts to undermine progress with smoke free laws or overturn existing laws via litigation; lack of compliance with laws; the need for monitoring and evaluation of smoke free laws; the need to make better use of mass media campaigns; and strengthening civil society. However, much progress has already been achieved to address these challenges, in particular through collaborations and the exchange of experience and expertise across Latin America.

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 score0.149
Threshold uncertainty score0.644

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)0.0010.000

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 head0.050
GPT teacher head0.344
Teacher spread0.294 · 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

Citations9
Published2010
Admission routes1
Has abstractyes

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