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Record W4409893292 · doi:10.11124/jbies-24-00569

Tools used to assess methodological quality of primary mixed methods or multi-method studies: a scoping review protocol

2025· review· en· W4409893292 on OpenAlexaff
Cindy Stern, Heather Loveday, Christina Godfrey, Danielle Pollock, Quan Hong, Kendra L. Rieger, Matthew Stephenson, Nisha Kurian, Jacopo Fiorini, Lucylynn Lizarondo

Bibliographic record

VenueJBI Evidence Synthesis · 2025
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsTrinity Western UniversityWestern UniversityCentre for Interdisciplinary Research in RehabilitationQueen's University
Fundersnot available
KeywordsCINAHLPsycINFOChecklistCritical appraisalData extractionProtocol (science)Systematic reviewScopusInclusion (mineral)MEDLINEComputer scienceMedicineManagement scienceData sciencePsychologyPsychological interventionAlternative medicineNursingEngineering

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this proposed scoping review is to identify the tools available to assess the methodological quality of primary mixed methods or multi-method studies, and to determine the type and extent of psychometric testing and properties evaluated. INTRODUCTION: Currently, JBI does not have an appraisal tool for primary mixed methods or multi-methods studies and recommends reviewers use the JBI qualitative tool and the relevant quantitative tool (based on study design) together. While useful, this does not allow reviewers to consider elements specifically related to the nuances of primary mixed methods studies. ELIGIBILITY CRITERIA: Any tool, checklist, scale, instrument, criteria, system, or framework that has been designed to assess the methodological quality of primary mixed methods or multi-methods studies will be of interest. Adapted or modified versions of tools will also be considered, and any psychometric properties measured will be recorded. Published and unpublished primary studies, reviews, and textual evidence are eligible for inclusion in the review. METHODS: The review will follow JBI methodology for scoping reviews and be reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). The following databases and resources will be searched: CINAHL Ultimate (EBSCOhost), PubMed, PsycINFO (OvidSP), Embase, Scopus, medRxiv, ProQuest Dissertations and Theses Global (ProQuest), OSF, and Google Scholar. Various websites will also be searched. No language limits will be placed. Screening, data extraction, and data analysis will be conducted by 2 reviewers independently. Descriptive statistics and basic content analysis will be used to convey the results of the review, supplemented by a narrative synthesis and presented in tabular and graphical format. REVIEW REGISTRATION: OSF https://osf.io/da9th.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptMetaresearch
Domain: Methods · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.343
metaresearch head score (Gemma)0.349
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.657
Threshold uncertainty score0.810

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3430.349
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0100.013
Bibliometrics0.0270.020
Science and technology studies0.0070.008
Scholarly communication0.0120.012
Open science0.0070.012
Research integrity0.0110.010
Insufficient payload (model declined to judge)0.0710.028

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 head0.971
GPT teacher head0.771
Teacher spread0.200 · 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

Labeled directly by 2 models reading the full record.

Study designSystematic review
DomainMethods
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".

Quick stats

Citations4
Published2025
Admission routes1
Has abstractyes

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