Dialectical behavior therapy skills training: Paving the way to healthy relationships
Bibliographic record
Abstract
Dialectical behavior therapy (DBT) is a comprehensive multiple mode cognitive-behavioral treatment. It includes weekly individual therapy, weekly group skills training, and as-needed phone coaching along with therapist consultation team meetings. DBT skills training is a central component of DBT effectiveness. Skills training programs have been reported to be effective in different populations, such as health care professionals, caregivers of the elderly, and college students. Skills training has also been effective to treat individuals with a range of mental disorders. The overall objective of the workshop is to describe a set of behavioral, cognitive and dialectical skills which can facilitate the development and maintenance of healthy relationships. Participants will be able to apply the principles of dialects, validation and behavioral analysis to their thoughts and actions; be able to develop effective communication; and find a kernel of truth in other people’s views. The skills presented are important for individuals with or without a diagnosis of mental disorder, and they can be helpful in any relationship. There are three skill sets: dialectics, validation, and behavior change strategies. Taken together, the skills focus on balancing our own priorities with the demands of others in interpersonal relationships. During the ninety-minute interactive workshop, skills will be presented alongside individual and small group exercises given by one presenter. The language of the workshop will be English, however questions can be asked in French, Spanish or Portuguese and will be answered in English.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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 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".