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Record W2025879034 · doi:10.1002/nvsm.185

AIDS prevention advertising: different message strategies for different communication objectives

2002· article· en· W2025879034 on OpenAlexaff
June Marchand, Pierre Filiatrault

Bibliographic record

VenueInternational Journal of Nonprofit and Voluntary Sector Marketing · 2002
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversité LavalUniversité du Québec à Montréal
Fundersnot available
KeywordsTask (project management)PsychologyAdvertisingSocial psychologyBusinessEngineering

Abstract

fetched live from OpenAlex

Abstract AIDS prevention, especially when it concerns young adults, is a very serious matter. As a topic to advertise, it is therefore particularly challenging. Public policy makers have the difficult task of finding the message strategy most likely to persuade the target group while, at the same time, showing consideration for parents' sensitivities. This study attempts to address this problem by testing three different message strategies on AIDS prevention: a rational strategy, an emotional strategy based on a negative emotion (fear), and an emotional strategy based on a positive emotion (rewarding improved behaviour). The results suggest that the rational message strategy generated more personal concern for AIDS prevention and the emotional message strategy based on a negative emotion presented a positive impact on behaviour intentions. Copyright © 2002 Henry Stewart Publications

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)0.004
metaresearch head score (Gemma)0.015
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.033
GPT teacher head0.272
Teacher spread0.238 · 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

Citations39
Published2002
Admission routes1
Has abstractyes

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