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Record W4378087266 · doi:10.3390/bs13060437

Gambling and Aging: An Overview of a Risky Behavior

2023· review· en· W4378087266 on OpenAlexaff
M. Fontaine, Céline Lemercier, Céline Bonnaire, Isabelle Giroux, Jacques Py, Isabelle Varescon, Valérie Le Floch

Bibliographic record

VenueBehavioral Sciences · 2023
Typereview
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversité Laval
FundersAgence Nationale de la Recherche
KeywordsProblematizationPsychologyNarrativePerspective (graphical)Narrative reviewPopulationAffect (linguistics)CognitionDevelopmental psychologySociologyPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Gambling is a field of study that has grown since the 2000s. Much research has focused on adolescents and youth as a vulnerable population. The rate of aging gamblers is increasing; however, evidence-based knowledge of this population is still too sparse. After introducing the issue (1), this article provides a narrative review of older adults' gambling through three sections: (2) older adult gamblers (age, characteristics, and motivations), (3) gambling as a risky decision-making situation, and (4) gambling disorder related to older adults. By drawing on the existing literature from a problematization perspective, this type of review can highlight complex and original research topics and provoke thought and controversy to generate avenues for future research. This narrative review provides an overview of the existing literature on gambling among older adults and offers perspectives on how aging can affect decision-making and thus gambling for this population. Older adults are a specific population, not only in terms of the consequences of gambling disorders but also in terms of the motivations and cognitions underlying gambling behaviors. Studies on behavioral science focusing on decision-making in older adults could help in the development of public policy in terms of targeted prevention.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.795
GPT teacher head0.626
Teacher spread0.169 · 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.

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

Citations7
Published2023
Admission routes1
Has abstractyes

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