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Record W3136620476 · doi:10.3917/spub.205.0473

Stigmatisation et santé publique : le côté obscur des interventions anti-tabac

2021· article· fr· W3136620476 on OpenAlexaff
Sébastien Saetta, Joëlle Kivits, Katherine L. Frohlich, Lætitia Minary

Bibliographic record

VenueSanté Publique · 2021
Typearticle
Languagefr
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

Tobacco control strategies, considered legitimate and effective, are rarely the subject of critical analysis in France. This is specifically true with regard to their potentially harmful effects, particularly against people who continue to smoke. This article introduces this debate, focusing on the potentially stigmatizing effects of anti-smoking policies. It has been attested by numerous international studies, and by a study in France, that the general process of tobacco denormalization has led to the stigmatization of smokers who then may be subject to discrimination. To the extent that smoking is now concentrated in the most disadvantaged socio-economic populations in France, the latter are thus more exposed to stigma. While underscoring the need to develop targeted interventions against them, this article also warns and calls for vigilance regarding the potential iatrogenic effects of these interventions. It is therefore necessary to develop research and evaluations on this subject in order to accurately measure the effects of these interventions, particularly in terms of stigmatization and self-stigma, and to ensure that public health actors do not generate more problems than they solve.

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)0.004
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.684
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.009
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.348
Teacher spread0.302 · 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.

Study designNot applicable
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

Citations5
Published2021
Admission routes1
Has abstractyes

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