Telepsychotherapy for generalized anxiety disorder: Impact on the working alliance.
Bibliographic record
Abstract
Telepsychotherapy represents a promising solution to problems pertaining to specialized mental health services accessibility, including when delivering psychotherapy to people who do not have access to care due to the COVID-19 pandemic. The quality of the working alliance established in such a therapeutic context remains often questioned. Moreover, no study has comparatively examined the evolution of the alliance over telepsychotherapy and conventional, face-to-face, psychotherapy. This study assesses the impact of cognitive- behavioral therapy administered via telepsychotherapy or face-to-face on the quality of the working alliance. One hundred and 15 participants suffering from generalized anxiety disorder (GAD) took part in this randomized controlled trial, 50 of whom were assigned to telepsychotherapy in videoconference and 65 of whom were assigned to conventional psychotherapy. Each client and their psychotherapist completed the Working Alliance Inventory every 2 sessions. In the current sample, telepsychotherapy did not interfere with the establishment of the working alliance over the course of the treatment for GAD. On the contrary, clients showed a stronger working alliance in telepsychotherapy delivered in videoconference than in conventional psychotherapy. Clients seemed to be more comfortable with telepsychotherapy than psychotherapists. The clinical implications of these findings 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 | 0.003 |
| 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) | 0.005 | 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".