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Record W2899268884 · doi:10.4309/jgi.2018.39.2

Prevention and Treatment of Problem Gambling Among Older Adults: A Scoping Review

2018· review· en· W2899268884 on OpenAlexaffvenue
Flora I. Matheson, Travis Sztainert, Yana Lakman, Sarah Jane Steele, Carolyn Ziegler, Peter Ferentzy

Bibliographic record

VenueJournal of Gambling Issues · 2018
Typereview
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCentre for Addiction and Mental HealthSt. Michael's HospitalGreoPublic Health OntarioUniversity of TorontoBrock University
Fundersnot available
KeywordsPsychologyPsychological interventionRecreationGerontologyOlder peopleInclusion (mineral)Inclusion and exclusion criteriaPsychiatryMedicineAlternative medicineSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

Gambling is a socially acceptable form of recreation for older adults, but excessive gambling can lead to negative financial consequences and mental health problems. The lack of attention given to gambling problems among older adults has been highlighted in the literature for over a decade. The objectives of this review were to examine relevant literature on interventions for prevention and treatment of problem gambling (PG) among older adults and to identify research gaps. To this end, we conducted a scoping review of both quantitative and qualitative research, focusing on adult studies. Because of the lack of PG research specific to older adults, we focused our review on prevention and treatment among adult studies that covered a wide age range. Our literature search, conducted in a range of bibliographic databases, located 7,632 titles. After duplicates were eliminated, 4,268 records remained; 2,321 were excluded based on title and 1,247 remained after abstract review. Three independent assessors reviewed the full text of 700 articles and found 247 that met our inclusion/exclusion criteria. We identified a paucity of research on prevention and treatment of problem gambling specific to older adults, with the gaps in evidence even greater for prevention. We found only six studies specific to adults aged 55 years and older. Studies on older women are severely lacking. We conclude with some suggestions for future research.RésuméLe jeu est une forme de loisir socialement acceptable pour les personnes en âge avancé, mais le jeu excessif peut entraîner des conséquences financières graves et des problèmes de santé mentale. Depuis plus d’une décennie, le manque d’attention accordée aux problèmes de jeu chez les personnes âgées a été souligné dans la littérature. Cette étude avait pour but d’examiner les ouvrages portant sur les interventions de prévention et le traitement du jeu problématique chez les personnes âgées et de cerner les lacunes dans la recherche. À cette fin, on a entrepris un examen de l’étendue de la recherche quantitative et qualitative axée sur les études sur les adultes. En raison de l’absence de recherche sur le jeu compulsif propre aux adultes âgés, nous avons axé notre examen sur la prévention et le traitement dans les études pour adultes couvrant plusieurs tranches d’âge. Notre revue de la littérature, menée dans diverses bases de données bibliographiques, a permis de répertorier 7 632 titres. Après l’élimination des doublons, il est resté 4268 titres; 2321 ont été exclus sur la base du titre et nous en avons conservé 1247 après la lecture des résumés. Trois évaluateurs indépendants ont examiné le texte intégral de 700 articles et ont repéré 247 articles qui répondaient à nos critères d’inclusion/d’exclusion. Nous avons constaté un manque de recherche sur la prévention et le traitement du jeu problématique propre aux personnes âgées, et des lacunes encore plus évidentes au chapitre de la prévention. Nous avons trouvé seulement six études portant spécifiquement sur les adultes âgés de 55 ans et plus. Les études sur les femmes d’âge mûr font cruellement défaut. Nous avons conclu en donnant quelques suggestions pour de futures recherches.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.919
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.000
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 head0.322
GPT teacher head0.520
Teacher spread0.198 · 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 designSystematic review
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

Citations20
Published2018
Admission routes2
Has abstractyes

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