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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".