Using the Online Psychotherapy Tool to Address Mental Health Problems in the Context of the COVID-19 Pandemic: Protocol for an Electronically Delivered Cognitive Behavioral Therapy Program
Notice bibliographique
Résumé
BACKGROUND: The considerable rise of mental health challenges during the COVID-19 pandemic has had detrimental effects on the public health sector and economy. To meet the overwhelming and growing demand for mental health care, innovative approaches must be employed to significantly expand mental health care delivery capacity. Although it is not feasible to increase the number of mental health care providers or hours they work in the short term, improving their time efficiency may be a viable solution. Virtually and digitally delivering psychotherapy, which has been shown to be efficient and clinically effective, might be a good method for addressing this growing demand. OBJECTIVE: This research protocol aims to evaluate the feasibility and efficacy of using an online, digital, asynchronous care model to treat mental health issues that are started or aggravated by stressors associated with the COVID-19 pandemic. METHODS: This nonrandomized controlled trial intervention will be delivered through the Online Psychotherapy Tool, a secure, cloud-based, digital mental health platform. Participants will be offered a 9-week electronically delivered cognitive behavioral therapy program that is tailored to address mental health problems in the context of the COVID-19 pandemic. This program will involve weekly self-guided educational material that provides an overview of behavioral skills and weekly homework. Participants (N=80) will receive personalized feedback from and weekly interaction with a therapist throughout the course of the program. The efficacy of the program will be evaluated using clinically validated symptomology questionnaires, which are to be completed by participants at baseline, week 5, and posttreatment. Inclusion criteria includes the capacity to consent; a primary diagnosis of generalized anxiety disorder or major depressive disorder, with symptoms that started or worsened during the COVID-19 pandemic; the ability to speak and read English; and consistent and reliable access to the internet. Exclusion criteria includes active psychosis, acute mania, severe alcohol or substance use disorder, and active suicidal or homicidal ideation. RESULTS: This study received funding in May 2020. Ethics approval was received in June 2020. The recruitment of participants began in June 2020. Participant recruitment is being conducted via social media, web-based communities, and physician referrals. To date, 58 participants have been recruited (intervention group: n=35; control group: n=23). Data collection is expected to conclude by the end of 2020. Analyses (ie, linear regression analysis for continuous outcomes and binomial regression analysis for categorical outcomes) are expected to be completed by February 2021. CONCLUSIONS: If proven feasible, this care delivery method could increase care capacity by up to fourfold. The findings from this study can potentially influence clinical practices and policies and increase accessibility to care during the COVID-19 pandemic, without sacrificing the quality of care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04476667; https://clinicaltrials.gov/ct2/show/NCT04476667. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/24913.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,023 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,055 | 0,011 |
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 ».