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Record W2157124751 · doi:10.4309/jgi.2003.8.14

Problem-solving skills in male and female problem gamblers

2003· article· en· W2157124751 on OpenAlexaffvenue
Diane Borsoi, Tony Toneatto

Bibliographic record

VenueJournal of Gambling Issues · 2003
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsychologyPathologicalImpulse control disorderDistressClinical psychologyDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

The current study was designed to compare the self-reported problem-solving skills of male and female gamblers. In total, 148 females and 112 males (mean age = 43.6 years, SD = 12.0), responding to an advertisement for people concerned about their gambling, completed the Problem Solving Inventory (Heppner, 1988). The PSI consists of three factors related to self-perception of problem-solving: confidence, personal control and approach-avoidance style. Gamblers were categorized into three subgroups according to their DSM-IV scores: Asymptomatic, Problem, and Pathological. Results from a series of analyses of co-variance (co-varying for the confounding effects of current emotional distress) revealed that gender had no significant effect, but problem severity on appraisal of problem-solving confidence and sense of personal control had a significant effect. Pathological gamblers were less confident and felt less in control than the other subgroups while engaging in problem-solving activities. Problem gamblers tended to have more negative appraisals of control than Asymptomatic gamblers. Problem-solving skills were also a significant predictor of DSM-IV scores for pathological gambling (i.e., negative appraisals were associated with higher DSM-IV scores). The results suggest that problem-solving skills are deficient in pathological gamblers and problem gamblers, but are not related to gender.

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 score0.101
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.134
GPT teacher head0.411
Teacher spread0.277 · 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

Citations6
Published2003
Admission routes2
Has abstractyes

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