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Record W1978462850 · doi:10.1080/1556035x.2013.727736

An All-Female Problem-Gambling Counseling Treatment: Perceptions of Effectiveness

2012· article· en· W1978462850 on OpenAlexaffabout
Noëlla Piquette, Erika Norman

Bibliographic record

VenueJournal of Groups in Addiction & Recovery · 2012
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsPsychologyThematic analysisGrounded theoryIntervention (counseling)Agency (philosophy)Clinical psychologyPerceptionPreferenceQualitative researchPsychiatrySociology

Abstract

fetched live from OpenAlex

Despite the fact that gambling is not gender-specific, the majority of the research in Western culture has focused on the situation of the American male gambler. Women of all ages, income, and culture gamble (Boughton, 2006; Statistics Canada, 2008; Wenzel & Dahl, 2009), thus emerging as the gender most likely to experience gambling difficulties (Li, 2007; Schnell, 2002), while simultaneously being less likely to seek treatment for problem gambling compared with men (Boughton; Volberg, 2003). A logical question arising is, “What intervention mechanisms would support female problem gamblers?” The purpose of this study was to explore women's experiences of all-female group counseling for problem gambling and to identify emergent themes from the women's experience. Participants were in a 12-week women-only treatment group offered through a provincial health agency. A qualitative, thematic analysis using the constant comparison method was conducted with resultant themes and grounded theory, providing insight into counseling practices for women problem gamblers. The results highlight that the women who participated in the group found women-only groups to be helpful and stated their preference for female-only treatment groups in the future. Further research and exploration of women-only treatment are recommended to improve problem-gambling intervention for women.

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 score0.060
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.063
GPT teacher head0.390
Teacher spread0.327 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

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