SY17-1 * HIGHLIGHTS FROM THE CANADIAN LONGITUDINAL STUDIES ON PROBLEM GAMBLING
Bibliographic record
Abstract
Introduction. To report on highlights of a longitudinal study of gamblers, the Alberta Leisure, Lifestyle, Lifecycle Project (LLLP) as well as comparisons with the Ontario Quinte Study. Method. Five LLL cohorts of gamblers (ages 13–15, 18–20, 23–25, 43–45, and 63–65) have been recruited through Random Digit Dialing (RDD) since February 2006. The cohorts are stratified by large and small urban centers and over-sampled for at-risk gamblers. Four data collections have occurred with initial telephone and face-to-face interviews, followed by web-based surveys. The selection of survey instruments reflected a biopsychosocial model of gambling. Results. Recruitment at Time 1: N = 1808 – Feb – Oct ′06; Time 2: N = 1495 – Nov ′07 – Jun ′08; Time 3: N = 1316 – Jul ′09 – Mar ′10; and Time 4: N = 1343 – Feb – Oct ′11. (Overall Retention Rate 75.1% – 20 deceased). In addition, N = 679 blood and saliva samples were collected. For comparison, the Quinte study had N = 4121 and a Retention Rate 90.4% over 5 time intervals. Highlights include. 1. an analysis of patterns of continuity/discontinuity of problem gambling over 5 years; 2. identification of variables best predicting future problem gambling, coordinated with the Quinte study. Conclusion. Longitudinal studies provide unique insights into the trajectory of gambling behaviors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".