Effectiveness of General Practitioner Referral Versus Self-Referral Pathways to Guided Internet-Delivered Cognitive Behavioral Therapy for Depression, Panic Disorder, and Social Anxiety Disorder: Naturalistic Study
Bibliographic record
Abstract
Background: Therapist-guided, internet-delivered cognitive behavioral therapy (guided ICBT) appears to be efficacious for depression, panic disorder (PD), and social anxiety disorder (SAD) in routine care clinical settings. However, implementation of guided ICBT in specialist mental health services is limited partly due to low referral rates from general practitioners (GP), which may stem from lack of awareness, limited knowledge of its effectiveness, or negative attitudes toward the treatment format. In response, self-referral systems were introduced in mental health care about a decade ago to improve access to care, yet little is known about how referral pathways may affect treatment outcomes in guided ICBT. Objective: This study aims to compare the overall treatment effectiveness of GP referral and self-referral to guided ICBT for patients with depression, PD, or SAD in a specialized routine care clinic. This study also explores if the treatment effectiveness varies between referral pathways and the respective diagnoses. Methods: This naturalistic open effectiveness study compares treatment outcomes from pretreatment to posttreatment and from pretreatment to 6-month follow-up across 2 referral pathways. All patients underwent module-based guided ICBT lasting up to 14 weeks. The modules covered psychoeducation, working with negative or automatic thoughts, exposure training, and relapse prevention. Patients received weekly therapist guidance through asynchronous messaging, with therapists spending an average of 10-30 minutes per patient per week. Patients self-reported symptoms before, during, immediately after, and 6 months posttreatment. Level and change in symptom severity were measured across all diagnoses. Results: In total, 460 patients met the inclusion criteria, of which 305 were GP-referred ("GP" group) and 155 were self-referred ("self" group). Across the total sample, about 60% were female, and patients had a mean age of 32 years and average duration of disorder of 10 years. We found no significant differences in pretreatment symptom levels between referral pathways and across the diagnoses. Estimated effect sizes based on linear mixed modeling showed large improvements from pretreatment to posttreatment and from pretreatment to follow-up across all diagnoses, with statistically significant differences between referral pathways (GP: 0.97-1.22 vs self: 1.34-1.58, P<.001-.002) and for the diagnoses separately: depression (GP: 0.86-1.26, self: 1.97-2.07, P<.001-.02), PD (GP: 1.32-1.60 vs self: 1.64-2.08, P=.06-.02) and SAD (GP: 0.80-0.99 vs self: 0.99-1.19, P=.18-.22). Conclusions: Self-referral to guided ICBT for depression and PD appears to yield greater treatment outcomes compared to GP referrals. We found no difference in outcome between referral pathway for SAD. This study underscores the potential of self-referral pathways to enhance access to evidence-based psychological treatment, improve treatment outcomes, and promote sustained engagement in specialist mental health services. Future studies should examine the effect of the self-referral pathway when it is implemented on a larger scale.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".