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Record W2180248207 · doi:10.1080/14459795.2015.1078392

Self-stigma coping and treatment-seeking in problem gambling

2015· article· en· W2180248207 on OpenAlexaff
Jenny D. Horch, David C. Hodgins

Bibliographic record

VenueInternational Gambling Studies · 2015
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsShamePsychologyHelp-seekingCoping (psychology)Clinical psychologyPath analysis (statistics)PopulationStigma (botany)PsychiatrySocial psychologyMental healthDemography

Abstract

fetched live from OpenAlex

Stigma has been explored as a cause of reduced and delayed treatment-seeking for problem gambling, a population in which only 1 in 10 seek treatment. The present study examined the effect of perceived public stigma and self-stigma on affect and behavioural coping efforts. Path analysis was used to examine self-stigma in 155 individuals with gambling problems. The majority of participants met criteria for a gambling disorder (93.5%), were current gamblers (69%) and had never sought treatment (54.2%). The data fit the proposed path model well; self-stigma was associated with reduced self-esteem and increased shame. Shame predicted use of secrecy and withdrawal coping. Endorsement of negative stereotypes of ‘problem gamblers’ was associated with decreased treatment-seeking while greater self-stigma predicted increased treatment-seeking. Additional predictors of increased treatment-seeking included greater gambling problem severity, more positive attitudes towards treatment, male sex and higher income. Self-stigma increased rather than decreased treatment-seeking in this analysis. Efforts to increase treatment-seeking could target women, those with lower income and those with less severe gambling problems.

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 score0.216
Threshold uncertainty score0.917

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)0.0000.000

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 head0.236
GPT teacher head0.461
Teacher spread0.225 · 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

Citations28
Published2015
Admission routes1
Has abstractyes

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