Bibliographic record
Abstract
Introduction Substantial empirical support for cognitive behavioral therapy (CBT) effectiveness in the treatment of various psychiatric disorders has been demonstrated. Adequate training in CBT results in improved therapist competence and patient outcomes. Essential part of the training in CBT is a clinical supervision. A review of theoretical perspectives on CBT supervision is provided. Commonly encountered obstacles in CBT supervision are illuminated with case examples. Objectives At the end of the presentation participants will be able to describe a theoretical approach to CBT supervision, list common obstacles encountered in CBT supervision and describe strategies to effectively address these obstacles. Aims The aim of the presentation is to encourage CBT psychotherapy supervisors to reflect on the supervisory methods they use and increase their ability to provide effective CBT supervision. Methods The literature on successful CBT supervision is reviewed. This case based presentation will illustrate strategies for addressing commonly encountered roadblocks in supervision. Results Having a theoretical framework for CBT supervision enhances supervisors’ ability to provide successful supervision. Conclusions Review of theoretical approaches to CBT supervision, the description of commonly encountered obstacles and strategies to manage them during the supervision creates platform for reflection on the supervisory methods used by the participants.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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; both teacher heads agree on what is shown here.
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".