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Record W2154878023 · doi:10.1177/070674370605101206

A Prospective Study of the Impact of Opening a Casino on Gambling Behaviours: 2- and 4-Year Follow-ups

2006· article· en· W2154878023 on OpenAlexaffvenueabout
Christian Jacques, Robert Ladouceur

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2006
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPsychologyContext (archaeology)Prospective cohort studyDemographySample (material)PsychiatryMedicineGeographySociologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVE: It is widely believed that the rate of pathological gambling is related to the accessibility and availability of gambling activities. Few empirical studies have yet been conducted to evaluate this hypothesis. Using a longitudinal prospective design, the current study evaluates the impact of a casino in Canada's Hull, Quebec region. METHOD: A random sample of respondents from Hull (experimental group) and from Quebec City (comparison group) completed the South Oaks Gambling Screen (SOGS) and gambling- related questions before the opening of the Hull Casino (pretest), 1 year after the opening (posttest), and on follow-up at Years 2 and 4. RESULTS: Although, 1 year after the opening of the casino, we did observe an increase in playing casino games and in the maximum amount of money lost in 1 day's gambling, this trend was not maintained over time (2- and 4-year follow-ups). In the Hull cohort, the rate of at-risk and probable pathological gamblers and the number of criteria on the SOGS did not increase at the 2- and 4-year follow-ups. The residents' reluctance to open a local casino was generally stable over time following the casino's opening. CONCLUSION: The discussion raises different explanatory factors and focuses on the context of the Regional Exposure Model as a potentially more applicable measure of studying the expansion of gambling.

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 score0.137
Threshold uncertainty score0.786

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 head0.051
GPT teacher head0.359
Teacher spread0.309 · 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

Citations49
Published2006
Admission routes3
Has abstractyes

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