Bibliographic record
Abstract
As gambling becomes more prevalent and more accessible in our society, pathological gambling is growing as a serious problem. In most instances, excessive gambling negatively affects a person's home, social and professional life, as well as leads to serious financial trouble. In repeated trials, cognitive-behavioural therapy has proven an extremely effective treatment for this problem. Written by the developers of an empirically supported CBT program for the treatment of pathological gambling, this Therapist Guide includes all the information and materials necessary to implement successful treatment. Most pathological gamblers exhibit misconceptions or erroneous beliefs about the nature of gambling, and one of the central points of focus of this treatment is to help clients correct these beliefs and understand the true nature of games of chance. The authors provide step-by-step instructions for clinicians to help clients understand all of the facets of their problem. In addition to correcting erroneous beliefs about gambling, this program teaches problem solving skills, self-assessment techniques, and trigger recognition and avoidance. Designed to be used in conjunction with the corresponding Workbook, this guide provides results not only in supervised therapy, but in long-term relapse prevention as well.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.133 | 0.107 |
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".