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Record W4293225162 · doi:10.1080/14459795.2022.2038655

An experiment on the perceived efficacy of fear-based messages in online roulette

2022· article· en· W4293225162 on OpenAlexaff
Seema Mutti-Packer, Hyoun S. Kim, Daniel S. McGrath, Emma V. Ritchie, Michael J. A. Wohl, Matthew Rockloff, David C. Hodgins

Bibliographic record

VenueInternational Gambling Studies · 2022
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsCarleton UniversityYork UniversityToronto Metropolitan UniversityUniversity of Calgary
Fundersnot available
KeywordsRoulettePsychologySocial psychologyCognitionAdvertising

Abstract

fetched live from OpenAlex

The current study examined the emotional and cognitive evaluations as well as the perceived efficacy of fear-based, text-only pop-up messages. The pop-up messages were presented when viewing a 3-minute prerecorded video of online roulette play. Fifty-nine people who gamble online viewed both low- and high-threat messages that reflected, by random assignment, either the financial (n= 27) or social (n= 32) consequences of gambling. Participants then reported their emotional and cognitive evaluations of the messages, as well as their perceived efficacy to facilitate responsible gambling. Eye-tracking was used as an objective measure of attention to the message. A 2 (message theme: social, financial) x 2 (threat level: low, high) mixed-model ANOVA was used to examine the evaluations and efficacy of the messages. The main effects of message theme/threat level were not significant. The 2 × 2 interaction for the outcome of overall effectiveness was significant, whereby the high-threat and social message combination was rated more effective than other combinations. For eye-tracking, there were no significant findings. The results suggest that fear-based social messaging may be more effective than non-fear inducing or financially-oriented messages. Further research can explore if messages that are perceived to be effective likewise lead to lower-risk gambling.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.673
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.209
GPT teacher head0.509
Teacher spread0.300 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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