Bibliographic record
Abstract
Currently, there are several different types of psychotherapeutic approaches that can be used with problem gamblers. Those most commonly used are cognitive-behavioral therapy (CBT), psychodynamic therapy, motivational interviewing, and supportive psychotherapy. Research into the effectiveness of each of these therapies is just now beginning. Demand for treatment services, however, is rising quickly in every developed country. Coupled with the fact that only a limited body of evidence supports pharmacotherapy for problem gambling is a pressing need for ongoing training and supervision in the therapy of problem gamblers. For instance, in the state of California, there are relatively few gamblingcertified specialists and there is no formalized network of clinical supervisors. To address this gap in training and need for supervision, Richard Bryant-Jefferies has written Counselling for Problem Gambling: Person-Centred Dialogues. (The author has written a series of Person-centred Dialgues books.) This text is designed to demonstrate the person-centered approach to counseling problem gamblers. It takes the reader through a series of dialogues, from the first therapy session. The book is geared toward those who treat problem gamblers, primarily therapists and counselors. The book describes the details of the therapy as it is applied to two patients with problematic gambling behaviors. Max, a slot machine and Internet gambler, initiates treatment, while Rob, a horsetrack bettor, is compelled to come to treatment at the request of his wife. Their counselors, Clive and Pat, utilize the same approach but have different counseling styles. Each counseling session is presented in dialogue format, giving the reader the sense of being present in the room. Most interesting are the highlighted boxes within session texts that summarize what is happening therapeutically with patient and therapist. Each chapter closes with a set of discussion questions that engage the reader to reflect further on that session. In addition to the patient dialogues, the book also has sections on supervision and sections that focus on the counselor's therapeutic techniques. The latter is a feature unique to this book, as it demonstrates that therapy for problem gamblers is not always unidirectional and that therapists also bring their own conceptions, distortions, and expectations about gambling to the therapy sessions.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".