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Record W1480533514 · doi:10.1177/1049732306296395

Promoting Public Health Messages: Should We Move Beyond Fear-Evoking Appeals in Road Safety?

2006· article· en· W1480533514 on OpenAlexaff
Ioni Lewis, Barry C. Watson, Katherine M. White, Richard Tay

Bibliographic record

VenueQualitative Health Research · 2006
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Calgary
FundersTransport Accident Commission
KeywordsFear appealFocus groupPublic healthPsychologyAppeal to emotionPublic relationsAdvertisingSuicide preventionHuman factors and ergonomicsSocial psychologyPoison controlPolitical scienceMedicineBusinessMarketingEnvironmental healthAppeal

Abstract

fetched live from OpenAlex

Road traffic injury is one of the most significant global public health issues of the 21st century. The extent to which negative, fear-evoking messages represent effective persuasive strategies remains a contentious public and empirical issue. Nevertheless, negative, fear-based appeals represent a frequently used approach in Australasian road safety advertising. The authors conducted a series of focus groups with 16 licensed drivers to explore the potential utility of appeals to emotions other than fear. More specifically, they sought to explore the utility of positive emotional appeals, such as those incorporating humor. The themes emerging from the qualitative analysis suggested that both emotion and the provision of strategies are key components contributing to the overall persuasiveness of a road safety advertisement. Overall, it appears there is support for researchers and health advertising practitioners to provide further attention to the role that positive emotional appeals might play in future campaigns.

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)0.046
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0460.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.001

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 head0.593
GPT teacher head0.633
Teacher spread0.040 · 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 designQualitative
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

Citations2
Published2006
Admission routes1
Has abstractyes

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