General-Purpose LLM Chatbots as Informal Step-0 Support: Systematic Review and Meta-analysis of Human–Human Social Connectedness Outcomes (Preprint)
Notice bibliographique
Résumé
BACKGROUND General-purpose artificial intelligence (AI) chatbots powered by large language models (LLMs) are increasingly used as always-available sources of companionship, advice, and emotional support. In real-world practice, this pattern can position everyday chatbots as a de facto “step 0” within emerging stepped-care ecosystems. However, the evidence base has emphasized symptoms and usability more than interpersonal processes, and it remains unclear whether AI-human interaction strengthens or erodes human–human social connectedness (eg, loneliness, perceived social support, interpersonal communication, empathy), outcomes that are plausibly proximal to disclosure and downstream help-seeking. OBJECTIVE This systematic review and meta-analysis aimed to synthesize quantitative evidence on the association between LLM chatbot interaction and human–human social connectedness across populations and study designs, and to identify measurement gaps that constrain inference about safe stepped-care integration. METHODS We conducted a PRISMA- and MOOSE-guided systematic review (PROSPERO registered) of studies published from January 1, 2022 onward. Searches were conducted in PubMed, Web of Science, Scopus, and PsycINFO, with an updated search in June 2025. We included quantitative and mixed-method/intervention studies in which LLM chatbot interaction was the primary exposure and social connectedness–related constructs were outcomes. Studies of embodied agents and legacy voice assistants were excluded. Two reviewers screened and extracted data, and risk of bias was assessed using the Newcastle-Ottawa Scale. We conducted three-level generic inverse-variance meta-analyses to pool effects separately for experimental/intervention studies (Cohen d) and cross-sectional studies (standardized β), with meta-regression testing moderation by participant sex (percentage male). RESULTS From 5302 records, 8 studies were eligible for meta-analysis (4 experimental/intervention; 4 cross-sectional). Experimental/intervention studies (174 participants) showed a large positive effect of AI-human interaction on social connectedness outcomes (Cohen d=1.29, 95% CI 1.06-1.51; k=11 effect sizes), with substantial heterogeneity (I²=70.1%). Cross-sectional studies (3325 participants) showed no statistically significant association between LLM chatbot use and social connectedness (β=-0.10, 95% CI -0.75 to 0.55; k=12), with extreme heterogeneity (I²=99.8%). Sex did not significantly moderate effects. Sensitivity analyses indicated that the pooled experimental effect was highly contingent on individual studies. CONCLUSIONS Structured, purpose-built AI-supported interactions can improve social connectedness–related outcomes under controlled conditions, but current evidence does not support assuming that every day, naturalistic chatbot use reliably enhances real-world human connectedness. Crucially for stepped-care framing, none of the included studies assessed help-seeking intentions or behavior, nor transitions from chatbot use to human sources of support, limiting inference about escalation, substitution, or safe “step 0” implementation. Future work should prioritize longitudinal and stepped-care designs with standardized interpersonal outcomes and validated help-seeking/escalation measures to determine whether everyday AI use augments human support or increases substitution risk. CLINICALTRIAL PROSPERO registration number: CRD42022325540
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 | 0,030 | 0,119 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,011 | 0,019 |
| Bibliométrie | 0,009 | 0,010 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».