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Record W7138482204 · doi:10.2196/preprints.82364

Updating the Good Reporting of a Mixed Methods Study (GRAMMS) Reporting Guidelines: Protocol for a Methodological Review and Modified Delphi Process (Preprint)

2025· article· W7138482204 on OpenAlexaboutno aff
Sarah Munce, Sergi Fàbregues, Quan Nha Hong, Alicia O’Cathain, Timothy Guetterman, John W. Creswell, Cheryl Poth, Katie N. Dainty, Andrea C Tricco, Mandy M. Archibald, Clementine Jarrett, Dorothy Luong, Frank Kiwanuka, Ahtisham Younas

Bibliographic record

Venuenot available
Typearticle
Language
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsMultimethodologyProtocol (science)ChecklistDelphi methodData collectionQuality (philosophy)Psychological interventionProcess (computing)Guideline

Abstract

fetched live from OpenAlex

BACKGROUND In mixed methods research (MMR), researchers combine elements of qualitative and quantitative methodologies, methods of data collection and analysis, viewpoints, and integration procedures to gain a deeper understanding of what is being studied, design culturally specific tools, and explore the conditions under which health care interventions succeed or fail. Integration is considered the hallmark of MMR and can occur at various levels, such as sampling, data collection, and analysis. MMR is particularly useful for investigating complex, multilevel programs and interventions and is well-suited to address research problems involving knowledge translation, program evaluations, or comparisons of therapeutic interventions. Although there are many potential benefits of mixed methods in health research, the extent to which mixed methods studies implement integration remains limited, with this specific gap persisting for almost 20 years. The Good Reporting of a Mixed Methods Study (GRAMMS) reporting guidelines were developed in 2008 to help improve the quality of reporting in mixed methods reports and articles. Since then, the field of mixed methods has evolved rapidly, and the guideline no longer reflects current practices and innovations. OBJECTIVE The objective of this study is to develop an updated GRAMMS 2.0 guideline. This project aims to develop the GRAMMS 2.0 guideline and checklist to improve consistency, transparency, and quality of reporting on studies that use mixed methods in health services research. The specific research objectives of this protocol are to (1) examine the extent of mixed methods methodological literature and identify relevant reporting quality criteria for inclusion in GRAMMS 2.0 (ie, a methodological review), and (2) using the results of the first objective, prioritize the components of the updated GRAMMS 2.0 (ie, modified Delphi approach and consensus meeting). METHODS This study will follow established methodological frameworks for reporting guideline development and will include a methodological review followed by a modified Delphi process. RESULTS The project has received funding from the Canadian Institutes of Health Research in April 2025. CONCLUSIONS The GRAMMS 2.0 guideline will improve the consistency, transparency, and quality of reporting of mixed methods studies by researchers and multiple knowledge users in policy and practice. Peer reviewers and editors may also use GRAMMS 2.0 to improve the review of manuscripts involving MMR. Ultimately, the updated guideline will increase the clarity of mixed methods research findings, thereby improving their potential transferability to practice and facilitating efficient use of new results in mixed methods in health research, bringing better returns on research investments. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/82364

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 imitation

Not 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.

metaresearch head score (Codex)0.589
metaresearch head score (Gemma)0.664
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.411
Threshold uncertainty score0.507

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5890.664
Meta-epidemiology (narrow)0.0040.007
Meta-epidemiology (broad)0.0070.013
Bibliometrics0.0150.013
Science and technology studies0.0060.008
Scholarly communication0.0100.009
Open science0.0070.013
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0200.012

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.

Opus teacher head0.928
GPT teacher head0.813
Teacher spread0.115 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreProtocol

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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