Artificial intelligence interventions in the mental healthcare of adolescents
Notice bibliographique
Résumé
Background: Adolescence is a critical phase in a person's life since it might affect behaviors and conditions that impact physical and mental health and contribute to adulthood illnesses. Primary care physicians (PCPs) are increasingly acknowledged for their critical role in detecting and managing adolescents' mental health problems. PCPs however face several challenges when providing mental healthcare to this population. Artificial Intelligence (AI) interventions may provide some solutions if they are adapted for primary care use. Given the rise and importance of mental health problems among adolescents, it is critical to identify such interventions and assess PCPs potential interest in using them. Objective: Two studies were conducted to respond to these objectives. The first sought to identify AI interventions tested and/or implemented in adolescents’ mental healthcare. The second explored perceived challenges of primary care physicians in providing adolescents’ mental healthcare, along with their perceived needs for AI interventions that would be helpful in dealing with adolescents’ mental health issues.Methods: In the first study a systematic scoping review was conducted to identify AI interventions tested and/or implemented in adolescents’ mental healthcare. Using the Levac et al. framework, we searched five electronic databases (MEDLINE, Embase, Web of Science Core Collection, Compendex, INSPEC) from inception date until February 2020. Two independent reviewers identified articles based on title and abstract, and full text. Inclusion criteria were patients aged 10 to 19 receiving mental healthcare from healthcare professionals (HCPs) and any HCP that provides for this demographic and listing of AI interventions that were tested and/or implemented. Outcomes were any related to patients, HCPs, or the healthcare system. Setting: any healthcare setting.The second study was a qualitative descriptive, based on focus group (FG) discussion with a sample of PCPs in the Montreal, Canada. Through purposeful sampling, we recruited four PCPs with specific interest in adolescent mental healthcare and AI interventions. FG discussions were conducted and audio-visually recorded through Zoom software and lasted 1 ¼ hours. The discussion was transcribed verbatim, followed by thematic analysis using A-priori and inductive coding.Results: Scoping review: 30 papers were retained for analysis from 1044 retrieved. AI interventions were most commonly reported for Autism Spectrum Disorder (n=3), Unspecified Outcomes of Psychological Stress/Pressure Level (n=3), Substance Use Disorder (n=2) and Dysfunctional Behavior (n=2). The application of AI within the continuum of mental healthcare for adolescents was used for the mediation of diagnostic processes (n=23), monitoring and evaluation (n=8), treatment (n=5), and prognosis (n=2). Focus Group study: PCPs saw AI interventions as potentially cost-effective, able to handle large amounts of data, and relatively credible. They envisioned AI to assist in collecting patients' data, suggesting a diagnosis, and establishing a treatment plan. However, they were concerned about these interventions' performances and outcomes and feared losing clinical competency. Participants highlighted systematic challenges PCPs face while giving care to adolescents, including parental involvement and psychosocial influences. PCPs desired interventions that were user-friendly. Conclusion: To implement AI appropriately, greater participation and critical understanding of patients', physicians', and data scientists' opinions on AI use in clinical processes is required, resulting in a feedback loop of co-designing future AI initiatives. It is predominantly believed that the promise of AI in adolescents' mental health was considerable. These first stages and analyses provide the foundation for future work examining the practical usability, application, and effectiveness of these interventions in adolescents' mental healthcare
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 | 0,014 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».