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Record W2122740974 · doi:10.4309/jgi.2005.14.9

Youth gambling: A public health perspective

2005· article· en· W2122740974 on OpenAlexaffvenue
Carmen Messerlian, Jeffrey L. Derevensky

Bibliographic record

VenueJournal of Gambling Issues · 2005
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsRealmPerspective (graphical)Public healthHealth promotionPublic relationsPositive Youth DevelopmentPromotion (chess)PsychologySociologyPolitical scienceMedicineDevelopmental psychologyNursingPolitics

Abstract

fetched live from OpenAlex

Over the last decade research in the area of youth gambling has led to a better understanding of the risk factors, trajectories and problems associated with this behaviour. At the same time, governments have begun to recognize the importance of youth gambling and have offered to support research and treatment programs. Yet, public health and prevention in the realm of youth gambling has only recently drawn the attention of researchers and health professionals. Early work by Korn and Shaffer (1999) set the groundwork for a public health approach to gambling. This paper attempts to apply health promotion theory to youth gambling and describes a conceptual framework and model. Strategies focus on addressing risk and protective factors through community mobilization, health communication, and policy development. It is anticipated that this paper will provide future directions and serve as a starting point for addressing youth gambling issues from this new perspective.

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)0.002
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0020.004
Scholarly communication0.0050.006
Open science0.0010.002
Research integrity0.0100.008
Insufficient payload (model declined to judge)0.0080.001

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 head0.516
GPT teacher head0.511
Teacher spread0.005 · 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

Citations29
Published2005
Admission routes2
Has abstractyes

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