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Record W2094639111 · doi:10.4000/communication.4786

Réception des campagnes de communication de santé publique et efficacité des messages suscitant de la peur

2003· article· fr· W2094639111 on OpenAlexvenueno aff
Didier Courbet

Bibliographic record

VenueCommunication · 2003
Typearticle
Languagefr
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophyArt

Abstract

fetched live from OpenAlex

L’article dresse, tout d’abord, un état critique des recherches sur l’influence des campagnes de communication de santé publique qui tentent de modifier des attitudes néfastes pour la santé des individus en utilisant l’appel à la peur. Il explique, ensuite, une étude expérimentale sur le rôle de la peur ressentie après plusieurs expositions à un message. Les résultats montrent que le danger assez fortement représenté n’a été efficace sur l’intention d’arrêter de fumer qu’avec plusieurs répétitions du message. De façon surprenante, les récepteurs qui se sentent les plus vulnérables quant au fait d’avoir un cancer sont ceux qui n’ont pas l’intention d’arrêter de fumer. Les sujets qui se sentent les moins vulnérables ont une forte intention d’arrêter de fumer. Les résultats sont discutés dans le cadre de la théorie des traitements biaisés de l’information.

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)0.013
metaresearch head score (Gemma)0.051
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: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.071
GPT teacher head0.447
Teacher spread0.376 · 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

Citations17
Published2003
Admission routes1
Has abstractyes

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