Predictors of Dropout From Cognitive-Behavioral Group Treatment for Panic Disorder With Agoraphobia
Bibliographic record
Abstract
Panic disorder and agoraphobia are both characterized by avoidance behaviors, which are known correlates of treatment discontinuation. The aim of this exploratory study is to distinguish the profile of participants suffering from panic disorder with agoraphobia that complete treatment from those who discontinue therapy by assessing four categories of predictor variables: the severity of the disorder, sociodemographic variables, participants' expectations, and dyadic adjustment. The sample included 77 individuals diagnosed with panic disorder with agoraphobia who completed a series of questionnaires and participated in a cognitive-behavioral group therapy consisting of 14 weekly sessions. Hierarchical linear regression analyses revealed the importance of anxiety, prognosis, and role expectations as well as some individual variables as predictors of therapeutic dropout, either before or during treatment. Among the most common reasons given by the 29 participants who discontinued therapy were scheduling conflicts, dissatisfaction with treatment, and conflicts with their marital partner. These results suggest that expectations and dyadic relationships have an impact on therapeutic discontinuation. The clinical implications of these findings are discussed.
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.
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 | 0.006 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".