MétaCan
Menu
Back to cohort
Record W2146955080 · doi:10.4309/jgi.2002.6.9

Characteristics of People Seeking Help from Specialized Programs for the Treatment of Problem Gambling in Ontario

2002· article· en· W2146955080 on OpenAlexaffvenueabout
Brian Rush, Raquel Shaw Moxam, Karen Urbanoski

Bibliographic record

VenueJournal of Gambling Issues · 2002
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsCollege of Physicians and Surgeons of OntarioCentre for Addiction and Mental Health
Fundersnot available
KeywordsDemographicsAgency (philosophy)Help-seekingPopulationPsychologySample (material)PsychiatryFamily medicineDemographyMedicineEnvironmental healthSociologyMental healthSocial science

Abstract

fetched live from OpenAlex

Objectives: The objectives of this study are to estimate the number of people seeking treatment on an annual basis in Ontario at specialized problem gambling treatment programs and describe important characteristics of clients. Method: Agency staff prospectively collected four broad information categories from clients: demographics, gambling activities, problem severity and services received, and submitted the data to a central database. Sample: The report includes submissions (total caseload equals 2224) from 44 designated problem gambling programs between January 1, 1998 and April 30, 2000. Results: Of the 2224 clients in treatment, 1625 (73.5%) were seeking help for their own gambling problem, and 504 (22.8%) were seeking help in dealing with a family member/significant other's gambling problem. The overall gender ratio of cases in treatment was about 1.4:1 (58.3% to 41.7%) males to females. A wide range of gambling activities was reported as problematic. Conclusion: Only a small percentage of people experiencing problems related to gambling are seeking help from specialized treatment programs. Population survey data are needed in Ontario to assess the potential over- or under- representation of particular sub-groups in treatment compared to the epidemiology of problem gambling in the community.

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 score0.078
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.278
GPT teacher head0.407
Teacher spread0.129 · 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

Citations35
Published2002
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Gambling IssuesSame topicGambling Behavior and TreatmentsFrench-language works237,207