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Record W2766338965 · doi:10.1111/1753-6405.12728

What do children observe and learn from televised sports betting advertisements? A qualitative study among Australian children

2017· article· en· W2766338965 on OpenAlexaff
Hannah Pitt, Samantha Thomas, Amy Bestman, Mike Daube, Jeffrey L. Derevensky

Bibliographic record

VenueAustralian and New Zealand Journal of Public Health · 2017
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsAdvertisingPsychologyQualitative researchMedicineEnvironmental healthSociologyBusiness

Abstract

fetched live from OpenAlex

OBJECTIVE: To explore children's awareness of sports betting advertising and how this advertising may influence children's attitudes, product knowledge and desire to try sports betting. METHODS: Semi-structured qualitative interviews were conducted with 48 children (8-16 years) from Melbourne, Victoria. The interview schedule explored children's recall and interpretations of sports betting advertising, strategies within advertisements that may appeal to children, children's product knowledge and understanding of betting terminology, and factors that may encourage gambling. Interviews were transcribed and thematic analysis was conducted. RESULTS: Children recalled in detail sports betting advertisements that they had seen, with humour the most engaging appeal strategy. They were also able to describe other specific appeal strategies and link these strategies to betting brands. Many children described how advertisements demonstrated how someone would place a bet, with some children recalling the detailed technical language associated with betting. CONCLUSIONS: Children had detailed recall of sports betting advertisements and an extensive knowledge of sports betting products and terminology. Implications for public health: To protect children from the potential harms associated with sports betting, governments should consider changing regulations and implementing evidence-based education campaigns to counter the positive messages children receive from the sports betting industry.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.180
GPT teacher head0.444
Teacher spread0.265 · 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 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

Citations59
Published2017
Admission routes1
Has abstractyes

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