Tools used to assess methodological quality of primary mixed methods or multi-method studies: a scoping review protocol
Bibliographic record
Abstract
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 Published and unpublished primary studies, reviews, and textual evidence are eligible for inclusion in the review.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.
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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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
| gpt | Metaresearch Domain: Methods · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.745 | 0.953 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.074 | 0.016 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".