Bibliographic record
Abstract
Decades of research supports the use of many different psychotherapies for patients with psychiatric disorders; therefore trainees and post-professionals need effective methods for learning psychotherapy and keeping up with the literature. New technologies can assist with this process. The purpose of this presentation is to present two, blended psychotherapy programs currently operating at McMaster University in Canada and Upstate Medical University in Syracuse, New York. At McMaster, a web-based program has been integrated into the standard curriculum. PTeR, or ‘psychotherapy training e-resources” contains several psychotherapy e-modules which contain video clips of specific therapies, power point presentations, reference materials, MCQs to assess knowledge base, an innovative interactive ‘virtual therapist” which assesses clinical competence, and guidelines for teaching psychotherapy. Preliminary results reveals residents and faculty can improve their knowledge base in psychotherapy, and participants report satisfaction with this e-resource. At Upstate university webcam supervision is being creatively utilized to record therapy sessions, and the OQ45, a patient rated instrument has been utilized to provide feedback to therapists. Webcam supervision permits more accurate feedback to be delivered to the trainee about process, content and specific therapeutic interventions and outcomes. This method of training is integrated with a very comprehensive program which teaches psychodynamic, CBT, DBT, family and many other therapies. This highly interactive workshop will present both these programs, discuss the use of e-learning in Europe, and provide participants with an opportunity to discuss incorporating some of these methods of training in their own training programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 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".