Online versus in‐person delivery of cognitive behaviour therapy for obsessive compulsive disorder: An examination of effectiveness
Bibliographic record
Abstract
Cognitive behavioural therapy (CBT) including exposure and response prevention is the first-line psychological treatment for obsessive compulsive disorder (OCD). Given changes in the clinical landscape, there are increasing efforts to evaluate its effectiveness in online contexts. Mirroring the traditional in-person delivery, few studies have assessed the role of therapist-guided, manual-based CBT for OCD delivered in real-time via videoconferencing methods. The present study sought to fill this gap by comparing in-person and online delivery of group-based CBT for the treatment of OCD. A convenience sample of participants with moderate to severe OCD (n = 144) were recruited from a naturalistic database from two large OCD specialty assessment and treatment centres. Patients received group-based CBT that was provided in-person (pre-COVID-19 pandemic; March 2018 to March 2020) or online via videoconferencing (during the COVID-19 pandemic; March 2020 to April 2021). In both delivery methods, treatment consisted of 2-h weekly sessions led by trained clinicians. Analyses revealed that, regardless of treatment modality, both in-person and online groups demonstrated significant, reliable, and statistically equivalent improvements in OCD symptoms post-treatment. Videoconferenced, clinician-led CBT may be a promising alternative to in-person delivery for those with moderate to severe OCD symptoms.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".