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Record W4389244415 · doi:10.1080/16066359.2023.2284204

Women who gamble online: a scoping review

2023· review· en· W4389244415 on OpenAlexaff
Emily Fillion, Eva Monson, Christine Loignon, Adèle Morvannou

Bibliographic record

VenueAddiction Research & Theory · 2023
Typereview
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPsychologyInternet privacyMedicineComputer science

Abstract

fetched live from OpenAlex

Online gambling is becoming ever-more prevalent. With research beginning to document that women are increasing their online gambling presence, there is a need to consolidate the evidence base to date with a view to better understand women’s specific experiences when gambling online. A scoping review was conducted to map and identify the existing literature surrounding women and their online gambling experiences and practices. Of 1235 articles found within eight databases, 13 were included based on their interest in women’s online gambling behavior. The review demonstrated the scarcity and uniqueness surrounding women’s experiences with online gambling, which is continuously increasing, and women are showing a preference for online casino games. Women live unique online gambling experiences, such that internal motivators (i.e. safety and anonymity) and external motivators (i.e. play for free) were identified among the variety of reasons that bring women to online gambling, in addition to the feelings of shame and guilt that women feel when gambling online. The results of this review highlight the many areas of gambling studies related to women that need more research investment, such as reducing the homogeneity of samples and online gambling activities, and prevention programming. Specifically, the current work suggests that treating women online gamblers as a homogeneous group does not allow us to understand the diversity of experiences when it comes to different women as well as different online gambling activities.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0100.021

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 head0.536
GPT teacher head0.607
Teacher spread0.070 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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

Citations1
Published2023
Admission routes1
Has abstractyes

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