Extension of the Consolidated Criteria for Reporting Qualitative Research Guideline to Large Language Models (COREQ+LLM): Protocol for a Multiphase Study
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
BACKGROUND: Qualitative research provides essential insights into human behaviors, perceptions, and experiences in health sciences. The COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist, published in 2007 and endorsed by the Enhancing the Quality and Transparency of Health Research Network, advanced transparency of qualitative research reporting. However, the recent integration of large language models (LLMs) into qualitative research introduces novel opportunities and methodological challenges that existing guidelines do not address. LLMs are increasingly applied to research design as well as processing, analysis, interpretation, and even direct interaction ("conversing") with qualitative data. However, their probabilistic nature, dependence on underlying training data, and susceptibility to hallucinations necessitate dedicated reporting to ensure transparency, reproducibility, and methodological validity. OBJECTIVE: This protocol outlines the methodological development process of COREQ+LLM, an extension to the COREQ checklist, to support transparent reporting of LLM use in qualitative research. The three main objectives are to (1) identify and categorize current applications of LLMs used as qualitative research tools, (2) assess how LLM use in qualitative studies in health care is reported in published studies, and (3) develop and refine reporting items for COREQ+LLM through a structured consensus process among international experts. METHODS: Following the Enhancing the Quality and Transparency of Health Research Network guidance for reporting guideline development, this study comprises 4 main phases. Phase 1 is a systematic scoping review of peer-reviewed literature from January 2020 to April 2025, examining the use and reporting of LLMs in qualitative research. The scoping review protocol was registered with the Open Science Framework on June 6, 2025, and will adhere to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Phase 2 will use a Delphi process to reach consensus on candidate items for inclusion in the COREQ+LLM checklist among an interdisciplinary international panel of experts. Phase 3 includes pilot testing, and phase 4 involves publication and dissemination. RESULTS: As of September 2025, the steering committee has been established, and the initial search strategy for the scoping review has identified 5049 records, with 4201 (83.20%) remaining after duplicate removal. Title and abstract screening is underway and will inform the initial draft of candidate checklist items. The COREQ+LLM extension is scheduled for completion by December 2025. CONCLUSIONS: The integration of LLMs in qualitative research requires dedicated reporting guidelines to ensure methodological rigor, transparency, and interpretability. COREQ+LLM will address current reporting gaps by offering specific guidance for documenting LLM integration in qualitative research workflows. The checklist will assist researchers in transparently documenting LLM use, support reviewers and editors in evaluating methodological quality, and foster trust in LLM-supported qualitative research. By December 2025, COREQ+LLM will provide a rigorously developed tool to enhance the transparency, validity, and reproducibility of LLM-supported qualitative studies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/78682.
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 | 0,077 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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