Comparing in-person to videoconference group cognitive behavioural therapy (CBT) for depressive disorders in an out-patient mood disorders clinic
Bibliographic record
Abstract
Abstract Background: Despite their considerable public health impact, most people with depressive disorders do not receive treatment due to barriers that limit access to high-quality care. Since the onset of the COVID-19 pandemic, depressive symptoms have sharply increased, and access-to-care barriers were magnified by physical distancing requirements. Videoconferencing is a virtual care modality that reduces access-to-care barriers and can be used to deliver cognitive behavioural therapy (CBT), an evidence-based treatment for depressive disorders. However, it is unclear whether videoconference CBT effectively decreases depressive symptoms, particularly in a group therapy format. Aim: This non-randomized study compared outcomes of group CBT for depressive disorders delivered via videoconference versus in-person. Method: Data on clinical outcomes (pre- and post-treatment depression, anxiety, and stress symptoms), treatment attendance, drop-out, and patient satisfaction were collected from adult outpatients of a mood disorders clinic who attended 14 weekly group CBT sessions either in-person (pre-pandemic; n =255) or via videoconference (during the pandemic; n =113). Results: Pre- to post-treatment decreases in depression, anxiety and stress symptoms did not differ between treatment modalities ( β =–.01–.06, p >.05). These effects were robust to patient-level factors (i.e. age, sex, co-morbidities, medication use). Moreover, videoconference group CBT was associated with higher attendance ( d =0.33) and lower drop-out (53% vs 70% of participants) compared with in-person group CBT. Conclusions: Videoconference group CBT for depressive disorders appears to be a promising and effective alternative to in-person CBT. However, these findings should be interpreted in light of the study’s non-randomized design and the potential confounding effects of the COVID-19 pandemic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".