Virtual versus Face-to-Face Cognitive Behavioral Treatment of Depression: Meta-Analytic Test of a Noninferiority Hypothesis and Men’s Mental Health Inequities
Bibliographic record
Abstract
Global rates of depression have increased significantly since the beginning of the COVID-19 pandemic. It is unclear how the recent shift of many mental health services to virtual platforms has impacted service users, especially for the male population which are significantly more likely to complete suicide than women. This paper presents the findings of a rapid meta-analytic research synthesis of 17 randomized controlled trials on the relative efficacy of virtual versus traditional face-to-face cognitive behavioral therapy (CBT) in mitigating symptoms of depression. Participants' aggregated depression scores were compared upon completion of the therapy (posttest) and longest follow-up measurement. The results supported the noninferiority hypothesis indicating that the two modes of CBT delivery are equally efficacious, but the results proved to be significantly heterogeneous indicating the presence of moderating effects. Indirect suggestive evidence was found to support moderation by gender; that is, depressed males may benefit more from virtual CBT. Perhaps, this field's most telling descriptive finding was that boys/men have been grossly underrepresented in its trials. Future trials ought to oversample those who have been at this field's margins to advance the next generation of knowledge, allowing us to best serve people of all genders, those who live in poverty, Indigenous, Black, and other Peoples of Colour, as well as any others at risk of being marginalized or oppressed in contemporary mental health care systems.
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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 | 0.052 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.031 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".