Extension of the Consolidated Criteria for Reporting Qualitative Research Guideline to Large Language Models (COREQ+LLM): Protocol for a Multiphase Study
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.245 | 0.297 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.080 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".