Smartphones and Mental Health Awareness and Utilization in a Low-Income Urban Community: Focus Group Study
Notice bibliographique
Résumé
Background: Mental health disorders pose a significant challenge in low- and middle-income countries (LMICs), contributing substantially to the global disease burden. Despite the high prevalence of these disorders, LMICs allocate less than 1% of health budgets to mental health, resulting in inadequate care and a severe shortage of professionals. Stigma and cultural misconceptions further hinder access to mental health services. These challenges are present in Bangladesh, with high prevalence rates of depression and anxiety, as well as a centralized and underresourced mental health care system. Digital tools, such as smartphone apps and online platforms, offer innovative solutions to these challenges by increasing accessibility, cost-effectiveness, and scalability of mental health interventions. Objective: This study aims to characterize the views around digital tools for mental health among residents of Korail (a major slum in Dhaka, Bangladesh), including the use of smartphones, and investigate acceptable digital tools and barriers and facilitators for digital mental health tools. Methods: A total of 8 focus group discussions were conducted with 38 participants, including individuals with serious mental disorders and their caregivers. The focus group discussions were guided using a semistructured topic guide, which included broad questions on smartphone usage to contextualize digital access, primarily focusing on perceptions of using mobile technology for mental health care. Focus groups were held in Bangla, audio recorded, and transcribed and translated in English. Data were analyzed using thematic analysis in NVivo 14. Results: Participants (mean age 37 y, SD 13.7) were mostly female (30/38, 79%), and 45% (17/38) personally owned smartphones, although 92% (35/38) reported smartphone access within the household. The findings revealed a general lack of awareness and understanding of digital mental health tools among slum residents. However, there was a notable appetite for these tools; participants recognized their potential to provide timely and cost-effective support, reduce hospital visits, and make health care more accessible. Participants highlighted the convenience and communication benefits of smartphones but expressed concerns about misuse such as excessive use, particularly among adolescents. Barriers to the utilization of digital mental health tools included limited technological literacy and accessibility issues. Despite these challenges, participants acknowledged the potential of these tools to bridge the gap in mental health services, especially for those unable to travel. The importance of providing proper guidance and education to maximize the effectiveness of digital tools was emphasized. Conclusions: Digital mental health tools hold promise for improving mental health care in underserved slum communities. This study underscores the need for further research and investment in tailored digital mental health solutions to address the unique needs of slum populations in LMICs.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».