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Record W4239237879 · doi:10.1515/nsad-2016-0002

Significant life events and social connectedness in Australian women's gambling experiences

2016· article· en· W4239237879 on OpenAlexaboutno aff
Elaine Nuske, Louise Holdsworth, Helen Breen

Bibliographic record

VenueNordic Studies on Alcohol and Drugs · 2016
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsSocial connectednessPsychologyRecreationCoping (psychology)Social psychologySocial capitalSocial supportQualitative researchDevelopmental psychologyClinical psychologySociology

Abstract

fetched live from OpenAlex

Aim The aim is to examine significant life events and social connections that encourage some women to gamble. Specifically, how do these events and connections described as important for women who develop gambling-related problems differ for women who remain recreational gamblers? Design 20 women who were electronic gaming machine (EGMs, poker machines, slots) players were interviewed using a brief interview guide. They also completed the nine question Problem Gambling Severity Index (PGSI) from the Canadian Problem Gambling Index CPGI). 11 women self-identified as recreational gamblers (RG) while 9 had sought and received help for their gambling problems (PG). Using a feminist, qualitative design and an adaptive grounded theory method to analyze their histories, a number of themes emerged indicating a progression to problem gambling for some and the ability to recognise when control over gambling was needed by others. Results Although both groups (RG and PG) reported common gambling motivations differences appeared in the strength of their social support networks and ways of coping with stress, especially stress associated with a significant life event. Conclusions The human need for social connectedness and personal bonds with others emphasised the usefulness of using social capital theories in gambling research with 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)0.000
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 score0.086
Threshold uncertainty score0.560

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 head0.168
GPT teacher head0.419
Teacher spread0.250 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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