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Record W2991136349 · doi:10.1080/14459795.2019.1697343

A longitudinal examination of gambling subtypes in young adulthood

2019· article· en· W2991136349 on OpenAlexaff
Damien A. Dowd, Matthew T. Keough, Lorna S. Jakobson, James M. Bolton, Jason D. Edgerton

Bibliographic record

VenueInternational Gambling Studies · 2019
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsYork UniversityUniversity of Manitoba
Fundersnot available
KeywordsPsychologyImpulsivityLatent class modelLongitudinal studyYoung adultAnxietyDepression (economics)Clinical psychologyDevelopmental psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

In previous research informed by the Pathways Model (an aetiological framework for problem and disordered gambling), latent mixture modelling was used to identify subtypes of gamblers based on measures of impulsivity, anxiety, depression, drug use, and alcohol dependence. The current study extended these findings by: (a) determining if similar subtypes would be identified in the same sample two years later; and (b) utilizing latent transition analysis (LTA) to determine if class membership remained stable over this time period. The sample (N = 566) included young adult gamblers. In line with previous work on Wave 2 of these data and theoretical considerations, a three class model of gamblers was retained at Wave 4: Non-Problem, Emotionally Vulnerable, and Impulsive. The LTA suggested that the majority of Non-Problem gamblers remained in the same class over time. In contrast, Emotionally Vulnerable gamblers were most likely to transition into the Non-Problem Gambler class, and Impulsive Gamblers were equally likely to transition into the Non-Problem and Emotionally Vulnerable classes. Our study provides evidence for the subtypes of gamblers outlined in the Pathways Model. It is also the first study to provide evidence that membership within Emotionally Vulnerable and Impulsive gambling subtypes is unstable during young adulthood.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.433
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.

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

Citations13
Published2019
Admission routes1
Has abstractyes

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