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Record W1972770988 · doi:10.1037/a0016846

Pathological gambling subtypes.

2009· article· en· W1972770988 on OpenAlex
David D. Vachon, R. Michael Bagby

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePsychological Assessment · 2009
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersOntario Problem Gambling Research Centre
KeywordsPsychologyPsychopathologyClinical psychologyTypologyPersonalityDisinhibitionPathologicalImpulse control disorderPsychiatrySocial psychologyInternal medicineMedicine

Abstract

fetched live from OpenAlex

Although pathological gambling (PG) is regarded in the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 1994) as a unitary diagnostic construct, it is likely composed of distinct subtypes. In the current report, the authors used cluster analyses of personality traits with a non-treatment-seeking community sample of gamblers and identified 3 PG subtypes. Gamblers partitioned into a simple PG cluster, characterized by low rates of comorbid psychopathology and trait scores near the normative mean; a hedonic PG cluster, characterized by moderate rates of comorbid psychopathology and a proclivity for excitement seeking and positive affect; and a demoralized PG cluster, characterized by high rates of comorbid psychopathology and a propensity toward negative affect, low positive emotionality, and disinhibition. The findings provide preliminary support for an empirically based typology of gamblers, distinguishable in terms of personality structure, which may reflect different etiologies.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score1.000

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)0.0060.001

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 head0.210
GPT teacher head0.513
Teacher spread0.303 · 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