Treatment Utilization by Problem Gamblers in Northwestern Ontario
Bibliographic record
Abstract
The Catalyst database, which is operated through the Ontario Centre for Addiction and \nMental Health, was used to explore factors that may be related to treatment non- \ncompliance and the number of admissions in the population of clients receiving addiction \ntreatment in Thunder Bay between 2003 and mid-2006. The distinction between Primary \nand Secondary Gamblers identified by Nguyen (2007) was explored to determine \nwhether this distinction is useful in predicting if the two groups differ in treatment non- \ncompliance and the number of admissions. A total of 2,743 clients were examined. \nComparisons were made between those who presented for treatment of gambling as their \nprimary problem (N = 138), those who presented for a substance addiction (N = 280) \nwith gambling as a secondary problem, and those who had only a substance addiction (N \n= 2,178). Non-compliant individuals are more likely to be gambling clients, younger, \nfemale, have a higher education level, better income source, better employment, and no \nlegal problems. An individual with more admissions to treatment is more likely to be a \nSecondary Gambler or Substance Problem Only client, older, have a poorer source of \nemployment and have legal problems. The distinction between primary and secondary \ngamblers was not found to be useful for predicting treatment non-compliance but did \npredict the number of admissions. It appears that these two outcome variables are \nmeasuring different aspects of treatment utilization and that it is important to consider \neach separately, as they both provide useful program planning information.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".