The Medium Becomes The Self: The Clinical Framework for Algorithmic Identity
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Over the last decade, mental health hospitalizations among young people (particularly Generation Z and the emerging Generation Alpha, born after 2012) have surged, with growing evidence linking this rise to problematic smartphone, social media, and now AI relational or companion use. Canadian data shows significant increases in hospital admissions for eating disorders, self-harm, and anxiety during high-risk periods such as the COVID-19 pandemic (Roumeliotis et al., 2024). Concurrently, research implicates the structure of digital platforms themselves in exacerbating depression, anxiety, disordered eating, and identity disturbance in Gen Z; appearance-driven platforms like TikTok and Instagram intensify social comparison, FOMO, compulsive self-monitoring, and cyberbullying (Shehab et al., 2025). As Gen Z has aged within this crisis, while Gen Alpha enters the same crisis a decade later, rates of underemployment, debt, emotional dysregulation, and overall life dissatisfaction continue to rise. Despite this now well-documented developmental emergency, mainstream mental health care models have not meaningfully adapted. Cognitive Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), and standard psychiatric assessments continue to overlook digital behaviours, algorithmic feedback loops, and AI-mediated interactions, both for Gen Alpha currently in crisis and for Gen Z living in its aftermath. From a media-ecology perspective, smartphones and social media are not neutral tools but environmental forces that reshape cognition, social perception, emotional regulation, and identity formation. Persistent engagement with interactive, appearance-focused platforms emerge as the Fourth Person (Robertson, 2025): a digital identity layer that exists alongside, but psychologically separate from, the offline self. This fragmentation amplifies anxiety, compulsive posting, regret, low self-worth, and interpersonal instability, yet it remains absent from clinical assessment frameworks. Emerging evidence further shows that AI “cyber-companion” systems cause psychosis, and/or intensify identity fragmentation by reinforcing the emotional, perceptual, and cognitive needs of the Fourth Person. These dynamics reveal a profound gap in current mental health care: digital and AI-mediated behaviours are not lifestyle preferences but core mechanisms of contemporary psychopathology. This thesis proposes a comprehensive, multi-level adaptation of mental health care that systematically integrates smartphone, social media, and AI use into assessment, diagnosis, and therapeutic intervention. Without such integration, the system will continue failing the very populations most harmed by the environments they were raised in; Gen Z after the crisis, and Gen Alpha entering the same crisis in real time.
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.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle