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
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».