Addressing the Ethical, Legal, and Social Issues of Healthtech in Education: Insights From Japan
Notice bibliographique
Résumé
The increasing application of health technology (healthtech) in educational settings, particularly for monitoring students' mental health, has garnered significant attention. These technologies, which range from wearable devices to digital mental health screenings, offer new opportunities for enhancing student well-being and strengthening support systems. Numerous studies have explored the ethical, legal, and social issues (ELSIs) of healthtech in the field of psychiatry, highlighting its potential benefits while also acknowledging the inherent complexities and risks that demand careful consideration. However, the ELSIs related to the use of healthtech in educational settings remain largely overlooked and insufficiently addressed. This study provides an overview of items that should be considered by researchers, teachers, and education boards or committees to promote healthtech in the educational context. By adapting existing ELSI frameworks from educational technology and digital health, this study systematically reviews ethical concerns surrounding healthtech in schools. Expert consultations were conducted through a project consisting of members with expertise related to healthtech, including developers, a teacher, a school counselor, and university researchers, leading to the identification of 52 ELSI concerns categorized into 8 domains: consent, rights and privacy, algorithms, information management, evaluation, use, role of public institutions, and relationships with private companies. Using Japan as a case study, we examine regulatory and cultural factors affecting healthtech adoption in schools. The findings reveal critical challenges, such as ensuring informed consent for minors, protecting student privacy, preventing biased algorithmic decision-making, and maintaining transparency in data management. In addition, institutional factors, including the role of public education policies and private-sector involvement, shape the ethical landscape of healthtech implementation. This study highlights the need for multistakeholder collaboration to establish guidelines that balance innovation with ethical responsibility. The study underscores the need for a multifaceted approach to mitigate risks such as data misuse, inequitable access, and algorithmic bias, ensuring the ethical and effective use of healthtech in education. The fundamental ELSI framework for healthtech, including privacy, consent, and algorithms, can be applied to educational systems worldwide, while aspects related to public education policies should be considered in accordance with the specific context of each country and culture. Incorporating healthtech into the educational system helps address the barriers associated with traditional approaches, including limited resources, cost constraints, and logistical challenges. Researchers from universities and healthtech companies, along with educators and other stakeholders, should ensure that healthtech projects consider diverse ELSI concerns at every stage before and during implementation.
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 | 0,019 | 0,016 |
| 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 | 0,014 | 0,016 |
| Communication savante | 0,008 | 0,006 |
| Science ouverte | 0,001 | 0,010 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».