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Record W4210664022 · doi:10.4309/jgi.2022.49.5

Early Detection of Gambling Among At-Risk Adolescents. Validation of EDGAR-A Scale

2022· article· en· W4210664022 on OpenAlexvenueno aff
Víctor Cabrera‐Perona, Daniel Lloret Irles, Rosa Núñez Núñez

Bibliographic record

VenueJournal of Gambling Issues · 2022
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyNormativeScale (ratio)Internal consistencyPsychological interventionConsistency (knowledge bases)PerceptionSocial psychologyRisk perceptionApplied psychologyClinical psychologyPsychiatryPsychometricsPolitical scienceComputer scienceLawCartography

Abstract

fetched live from OpenAlex

Despite the fact that minors have prohibited access to commercial gambling, and legislation trying to constrain gambling, an important proportion declares that they have bet either online, or by illegally entering gambling venues. This situation highlights the need to implement selective prevention programs that requires assessment tools to identify vulnerable groups. This paper aims to design and validate a scale of evaluation for the psycho-social characteristics that predict onset and maintenance of gambling behavior among adolescents. 2,716 students of Secondary Education, 15.12 years (± 1.03) answered a frequency, intensity and problematic gambling questionnaire and a scale to evaluate risk profiles. The resulting scale is compounded by 26 items classified in 4 sub-scales: Accessibility, Risk Perception, Normative Perception and Parental Attitudes. Internal consistency coefficients were: 0.668, 0.728, 0.746 and 0.818 respectively, and 0.811 for the total scale. Results offer a robust support on the structural validity and internal consistency of the Early Detection of Gambling among At-Risk Adolescents (EDGAR-A) Scale, a useful tool for the design and assessment of effective preventive interventions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.734

Codex and Gemma teacher scores by category

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

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 head0.133
GPT teacher head0.396
Teacher spread0.263 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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