Creating fast-access to effective psychotherapy: Evaluation of virtual clinician assisted bibliotherapy for low mood and depression in Ontario. (Preprint)
Notice bibliographique
Résumé
BACKGROUND Ontario faces a significant gap in effective, affordable, and accessible mental health treatments, leaving millions with unmet needs. The demand for mental health services that are both accessible and cost-effective far exceeds the available supply. To address this need, Canadian Mental Health Association, York and South Simcoe Regions (CMHA-YSS) launched a large-scale, virtual, cognitive behavioural therapy (CBT)-based treatment, the Clinician Assisted Bibliotherapy (CAB) program. CAB was designed to be an entry-level treatment within the stepped-care model to support Ontarians' mental health. OBJECTIVE CAB follows a blended model format that combines virtual clinician support with structured reading materials, at no cost to the participant. Psychotherapy sessions were virtual for increased accessibility and focused on guided discovery of key concepts, facilitated behavioral exercises, motivation enhancement when needed, and addressed any additional questions. This study aimed to evaluate the effectiveness of a CAB trial program by: (1) assessing changes in participants’ self-reported symptoms of depression, anxiety, and functional impairment, and (2) calculating rates of recovery and reliable improvement. METHODS This study applied a pragmatism lens, commonly used in program evaluations and effectiveness studies, emphasizing actionable, context-specific knowledge for routine clinical settings. Therefore, participants were recruited through clinician referrals, community outreach, and digital platform-based self or primary care provider referrals, ensuring accessibility and reflecting real-world pathways to mental health care. Three hundred and eighty-three individuals were referred to CAB in 2020-2021; of these, 299 were included in this study. The participants were Ontarians who reported a primary concern of low mood or depression. Multilevel Modelling (MLM) was used to evaluate changes throughout the program's duration in self-reported symptoms of depression, anxiety, and functional impairment. Rates for recovery and reliable improvement were calculated and compared to existing programs. RESULTS Analyses confirmed that the data met all model assumptions, with no violations detected. The program’s retention rate was 59%. Multilevel modeling results supported our hypotheses, showing improvements in depression, anxiety, and functional impairment throughout therapy. Therapy dose moderated symptom reduction, with additional sessions leading to a more gradual decline in depression and anxiety scores. Recovery and reliable improvement rates were robust and comparable to traditional psychotherapy, reinforcing CAB’s effectiveness in reducing mental health symptoms. CONCLUSIONS This study highlights the effectiveness of virtual, low-intensity psychotherapy, showing that brief, guided interventions can maintain the principles of evidence-based CBT while overcoming common barriers, such as location, transportation, cost, and scheduling. By positioning CAB within a real-world community mental health context, this study expands the evidence base for virtual bibliotherapy and supports its integration into scalable, stepped-care service models, ensuring that individuals with varying levels of need can receive timely and appropriate treatment.
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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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».