Implementing Digital Tools for Mental Health Support in Young Individuals in Colombia: Mixed Methods Feasibility Study
Notice bibliographique
Résumé
Background: The growing prevalence of mental health disorders among young people is a pressing global concern, particularly in low- and middle-income countries where access to care is limited. Digital tools, leveraging Information and Communication Technologies, offer promising approaches to bridge these gaps. Objective: This study evaluated the feasibility of 2 digital mental health tools-Youth Collective Minds (YMC), a web-based platform, and Mental Beat (MB; Avicenna Research), a smartphone app-targeted at young individuals aged 18-25 years in Bogotá, Colombia. Methods: Participants (N=35) engaged with both platforms over 3 weeks in this mixed methods feasibility study, which incorporated thematic analysis with a deductive framework for qualitative data. Univariate analyses were performed to examine baseline patterns and data distributions, while bivariate analyses were conducted to investigate relationships and associations between variables, providing a comprehensive evaluation of the platforms' feasibility in the acceptability, demand, implementation, and practicality domains. Results: Participants were primarily women (22/35, 63%) with a median age of 23 (IQR 21-24) years. A total of 1308 annotations were coded: acceptability (annotations=707), demand (annotations=116), implementation (annotations=276), and practicality (annotations=209). Participants highlighted YMC's psychoeducational resources and MB's ease of use as strengths. However, technical issues, including server malfunctions and insufficient feedback, impacted engagement. Quantitatively, 83% (29/35) expressed willingness to reuse YMC and 83% (29/35) MB. Sensor data from MB indicated significant associations between psychological distress and smartphone usage. Participants with higher psychological distress showed greater median battery charging of 585 (IQR 321-615) compared to those without distress, 188 (IQR 42-309; P=.04). Poor sleep quality was also associated with increased median battery discharge of 2867 (IQR 1697.5-3935.5) compared to participants who reported sufficient sleep, 556 (IQR 200-2968; P=.003). GPS data showed that participants who visited more unique locations had lower psychological distress scores, with a negative correlation (r=-0.424; P=.05). In terms of platform usage, in YMC, surveys on emotions (30/35, 86%) and stress (28/35, 80%) were the most frequently completed, while telecounseling services were underused, with only 8.6% (3/35) of participants accessing mental health telecounseling. In MB, surveys of positive emotions (97.1%) and relationships (97.1%) were answered by more than 90% (32/35) of participants. Conclusions: This study demonstrated the feasibility and acceptability of digital tools for mental health support among Colombian youth, suggesting that these tools promote self-awareness and mental health management but require technical refinements to enhance engagement. The study's limitations, including a small sample size and short duration, underscore the need for broader research. Implementing participant feedback, strengthening cybersecurity, and scaling these tools could address mental health challenges in low- and middle-income countries, where such interventions are critically needed. These digital platforms represent promising steps toward bridging gaps in mental health care access.
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 | 0,008 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».