Cognitive Behavior Therapy for Panic Disorder with Agoraphobia in Videoconference: Preliminary Results
Bibliographic record
Abstract
Many studies have shown the feasibility of psychiatric consultation in telehealth, and some have addressed the effectiveness of telepsychotherapy. However, outcome studies on telepsychiatry essentially amount to a few case studies, none of which have used an empirically validated psychosocial treatment to treat a specific mental disorder. This article presents the preliminary results of an outcome study on the effectiveness of telepsychotherapy for panic disorder with agoraphobia. Participants received 12 sessions of cognitive-behavior therapy, which is an empirically validated treatment for panic disorder with agoraphobia. The treatment was delivered via videoconference by trained therapists according to a standardized treatment manual. The remote site was located at 130 km north of the local site and both were linked by six ISDN lines. Telepsychotherapy demonstrated statistically and clinically significant improvements on measures of target symptoms (frequency, of panic attacks, panic apprehension, severity of panic disorder, perceived self-efficacy) and measures of global functioning (trait anxiety, general improvement). Of interest was the fact that a very good therapeutic alliance was built after only the first telepsychotherapy session. Factors that may reduce the effectiveness of telepsychotherapy are discussed.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".