Technology Use During the COVID-19 Pandemic and the Ways in Which Technology Can Support Adolescent Well-being: Qualitative Exploratory Study
Notice bibliographique
Résumé
BACKGROUND: Most adolescents in the United States engage with technology. Social isolation and disruptions in activities owing to the COVID-19 pandemic have been linked to worsening mood and overall decreased well-being in adolescents. Although studies on the direct impacts of technology on adolescent well-being and mental health are inconclusive, there are both positive and negative associations depending on various factors, such as how the technology is used and by whom under certain settings. OBJECTIVE: This study applied a strengths-based approach and focused on the potential to leverage technology to benefit adolescent well-being during a public health emergency. This study aimed to gain an initial and nuanced understanding of how adolescents have used technology to support their wellness throughout the pandemic. In addition, this study aimed to further motivate future large-scale research on how technology can be leveraged to benefit adolescent well-being. METHODS: This study used an exploratory qualitative approach and was conducted in 2 phases. Phase 1 consisted of interviewing subject matter experts who work with adolescents to inform the creation of a semistructured interview for phase 2. Subject matter experts were recruited from existing connections with the Hemera Foundation and National Mental Health Innovation Center's (NMHIC) networks. In phase 2, adolescents (aged 14-18 years) were recruited nationally through social media (eg, Facebook, Twitter, LinkedIn, and Instagram) and via email to institutions (eg, high schools, hospitals, and health technology companies). High school and early college interns at NMHIC led the interviews via Zoom (Zoom Video Communications) with an NMHIC staff member on the call in an observational role. A total of 50 adolescents completed interviews regarding their technology use and its role during the COVID-19 pandemic. RESULTS: The overarching themes identified from the data were COVID-19's impact on adolescent lives, positive role of technology, negative role of technology, and resiliency. Adolescents engaged with technology as a way to foster and maintain connection in a time of extended isolation. However, they also demonstrated an awareness of when technology was negatively affecting their well-being, prompting them to turn to other fulfilling activities that do not involve technology. CONCLUSIONS: This study highlights how adolescents have used technology for well-being throughout the COVID-19 pandemic. Guidelines based on insights from the results of this study were created for adolescents, parents, caregivers, and teachers to provide recommendations for how adolescents can use technology to bolster overall well-being. Adolescents' ability to recognize when they need to engage in nontechnology-related activities and their ability to use technology to reach a larger community indicate that technology can be facilitated in positive ways to benefit their overall well-being. Future research should focus on increasing the generalizability of recommendations and identifying additional ways to leverage mental health technologies.
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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 | 0,015 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,007 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,003 |
| 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 ».