Improving the content validity of the mixed methods appraisal tool: a modified e-Delphi study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The mixed methods appraisal tool (MMAT) was developed for critically appraising different study designs. This study aimed to improve the content validity of three of the five categories of studies in the MMAT by identifying relevant methodological criteria for appraising the quality of qualitative, survey, and mixed methods studies. STUDY DESIGN AND SETTING: First, we performed a literature review to identify critical appraisal tools and extract methodological criteria. Second, we conducted a two-round modified e-Delphi technique. We asked three method-specific panels of experts to rate the relevance of each criterion on a five-point Likert scale. RESULTS: A total of 383 criteria were extracted from 18 critical appraisal tools and a literature review on the quality of mixed methods studies, and 60 were retained. In the first and second rounds of the e-Delphi, 73 and 56 experts participated, respectively. Consensus was reached for six qualitative criteria, eight survey criteria, and seven mixed methods criteria. These results led to modifications of eight of the 11 MMAT (version 2011) criteria. Specifically, we reformulated two criteria, replaced four, and removed two. Moreover, we added six new criteria. CONCLUSION: Results of this study led to improve the content validity of this tool, revise it, and propose a new version (MMAT version 2018).
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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 | 0.404 | 0.624 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it