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Record W2899454683 · doi:10.4309/jgi.2018.39.8

Gambling Disorder in the College Student-Athlete Population: An Overview

2018· article· en· W2899454683 on OpenAlexvenueno aff
Donald E. Nowak

Bibliographic record

VenueJournal of Gambling Issues · 2018
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPsycINFOBasketballAthletesPsychologyPopulationClinical psychologyMental healthMEDLINEPsychiatryMedicinePhysical therapyPolitical science

Abstract

fetched live from OpenAlex

This review examines research from 1991 to the present regarding college student-athlete gambling addiction and disorder issues, with an emphasis on prevalence rates, motivations, and comorbid disorders, as well as National Collegiate Athletic Association (NCAA) national studies and derivative research. Subsets of the college student-athlete population, specifically minority athletes, are also examined. Databases PsycINFO, PsycARTICLES, ERIC, SPORTDiscus, MEDLINE, and Dissertation Abstracts International (ProQuest), were searched for possible contributions to this review. It was determined that student-athletes, and male student-athletes in particular, are vulnerable to disordered gambling problems, which, if university administration and athletic departments do not address, may result in severe negative consequences for the student-athlete. The research suggests that, for the most part, student-athletes have a higher rate of pathological gambling than non-athletes, though the rate of “normal” gambling behavior is about the same. Additionally, it appears that athletes in certain high profile team sports (football, basketball, etc.), as well as athletes belonging to a minority group, are more likely to report problems with gambling than their counterparts. Recommendations for working with student-athletes with a gambling disorder, as well as directions for future research in this burgeoning area, are offered. These proposals include screening for the disorder by mental health professionals and counsellors, as well as training for coaches and financial aid personnel.Résumé Cette étude fait l’examen de la recherche effectuée de 1991 à nos jours sur les problèmes de dépendance et de désordre chez les étudiants-athlètes, en mettant l’accent sur les taux de prévalence, les motivations et les troubles concomitants, ainsi que les études nationales de la National Collegiate Athletic Association et des travaux de recherche dérivés. Des sous-ensembles de la population d’étudiants-athlètes universitaires, en particulier des athlètes faisant partie de minorités, sont également sousmis à l’étude. Des recherches ont été faites dans les bases de données PsycINFO, PsycARTICLES, ERIC, SPORTDiscus, MEDLINE et Dissertation Abstracts International (ProQuest) pour trouver d’éventuelles contributions à la présente étude. On a établi que les étudiants-athlètes, masculins en particulier, sont vulnérables aux problèmes de jeu compulsifs, et s’ils ne sont pas pris en main par l’administration universitaire et les départements sportifs, ces troubles peuvent avoir de graves conséquences pour eux. La recherche laisse entendre que, pour la plupart, les étudiants-athlètes ont un taux de jeu pathologique plus élevé que les non-athlètes, bien que le taux de jeu « normal » soit à peu près le même. De plus, il semble que les athlètes de certains sports d’équipe de haut niveau (football, basketball, etc.), ainsi que les athlètes appartenant à un groupe minoritaire, sont plus susceptibles de montrer des problèmes de jeu que leurs homologues. Des recommandations sont faites pour travailler avec des étudiants-athlètes ayant un trouble du jeu, ainsi que des orientations pour de futures recherches dans ce domaine en progression. Ces propositions comprennent le dépistage du trouble par des professionnels de la santé mentale et des conseillers, ainsi que la formation des entraîneurs et du personnel de l’aide financière.

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 score0.012
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.375
GPT teacher head0.522
Teacher spread0.147 · 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

Citations7
Published2018
Admission routes1
Has abstractyes

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