Evidence for Digital Mental Health Assessment Tools in the Post–COVID-19 Era: Protocol for a Systematic Review on Diagnostic Accuracy Across Age Groups
Notice bibliographique
Résumé
Background: Digital assessment tools in health care are increasingly used to aid clinicians in diagnosing mental health conditions. Particularly since the quarantine and isolation guidelines of the COVID-19 pandemic moved much of health care online, there has been an accelerated adoption of digital assessment tools. The diagnostic accuracy of digital mental health assessment tools for a range of psychiatric conditions has yet to be fully explored, especially for their use in populations of older adults and children. Objective: This systematic review aims to (1) summarize recent studies on digital self-report question-and-answer-based mental health assessment tools for use in all ages across a range of psychiatric conditions (eg, the type and number of questions, if available; reference tests; timing; and blinding procedures), (2) present their validity (ie, diagnostic accuracy), and (3) assess study quality and applicability. Methods: The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guided the development of this protocol. The protocol has been registered with PROSPERO. The searches were guided by the PICO (population, intervention, comparator, and outcome) framework. A systematic search was conducted of the following databases of literature published since 2021: MEDLINE, Embase, Cochrane Library, ASSIA, Web of Science Core Collection, CINAHL, and PsycINFO. Searches of clinical trial databases and hand searching of reference lists will be completed. Two authors have independently screened titles and abstracts of identified papers and selected studies according to eligibility criteria, resolving inconsistencies through discussion. Full texts were screened following the same process. The authors extracted data using the Covidence data extraction tool (Veritas Health Innovation Ltd; eg, sensitivity and specificity). Two authors will use the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool to assess risk of bias for each full-text inclusion. Results: Scoping for this review began in December 2024. Searches of databases were completed in January 2025. Full-text screening and identification of the relevant gray literature were completed by the end of August 2025, and the final review is expected to be completed by December 2025. Conclusions: The review aims to present the validity and quality of the diagnostic accuracy of digital mental health assessment tools across different ages (including children and older adults), particularly following the COVID-19 pandemic due to the exponential increase in development and use of such tools. This review will provide evidence for the wider deployment of digital mental health assessment tools across a wide age range. There will also be a discussion about future research for digital tools and avenues for policy around digital mental health assessments.
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 | 0,065 | 0,107 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,005 |
| Méta-épidémiologie (sens large) | 0,022 | 0,024 |
| Bibliométrie | 0,017 | 0,015 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,009 | 0,011 |
| Science ouverte | 0,005 | 0,006 |
| Intégrité de la recherche | 0,008 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,056 | 0,006 |
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 ».