Tratamento cognitivo e comportamental para transtornos do controle de impulsos
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: This paper reviews the cognitive-behavioral treatment of kleptomania, compulsive buying, and pathological gambling. METHOD: A review of the published literature was conducted. RESULTS: Treatment research in all of these areas is limited. The cognitive-behavioral techniques used in the treatment of kleptomania encompass covert sensitization, imaginal desensitization, systematic desensitization, aversion therapy, relaxation training, and alternative sources of satisfaction. Regarding compulsive buying, no empirical support for treatment exists but common techniques examined were covert sensitization, exposure and response prevention, stimulus control, cognitive restructuring, and relapse prevention. Treatment of pathological gambling has been successful in both group and individual format using techniques such as aversive therapy, systematic desensitization, imaginal desensitization and multimodal behavior therapy (which have included in vivo exposure, stimulus control, and covert sensitization) along with cognitive techniques such as psychoeducation, cognitive-restructuring, and relapse prevention. CONCLUSIONS: There is a general consensus in the literature that cognitive-behavioral therapies offer an effective model for intervention for all these disorders. An individualized case formulation is presented with a case study example. Clinical practice guidelines are suggested for each disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 it