A Baseline Study of Past-Year Problem Gambling Prevalence Among Ohioans
Bibliographic record
Abstract
Historically, the scope of legalized gambling was limited in Ohio, but everything changed when a new constitutional amendment allowed four casinos to open. To better understand the impact of gambling expansion, a household survey was commissioned to determine the baseline estimate of problem gambling behaviours in the state before casinos opened. Participants were selected through multi-stage probability sampling, with over 3,500 respondents completing valid surveys. Nearly 60% of Ohioans gambled in the past year, but the statewide prevalence of problem gambling was relatively low; only 1.4% of persons scored high enough on the Problem Gambling Severity Index to be classified as a potential problem gambler (score >3). Regional estimates of problem gambling were highest for Franklin and Hamilton counties (both 5.0%) and lowest for Lucas and Cuyahoga counties (3.2% and 2.1%, respectively). Exploratory logistic regression modelling found that race, employment, education, family history of problem gambling, and feelings of depression increased the odds of being a problem gambler. Results will inform the discussion about current gambling problems and enable policy makers to design prevention strategies.La présente étude portait sur la relation entre le prix des billets de loterie instantanée (à gratter) et la « récupération des pertes » dans un seul épisode de jeu. Pendant plusieurs mois, chaque fois qu’un billet de loterie instantanée était acheté (N = 1081), les commis de dépanneur consignaient le sexe des joueurs et le prix des billets de loterie instantanée, et indiquaient si le consommateur avait acheté un autre billet avant de quitter les lieux. L’analyse de régression logistique a montré une corrélation importante entre le prix des billets et le rachat (rapport de cotes = 0,842, p < 0,0001), ce qui suggère que la récupération au cours d’un même épisode de jeu est courante pour les billets de loterie instantanée à plus bas prix et que de plus faibles coûts ne réduisent pas nécessairement les risques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".