Improving the usefulness of a tool for appraising the quality of qualitative, quantitative and mixed methods studies, the <scp>Mixed Methods Appraisal Tool</scp> (<scp>MMAT</scp>)
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Systematic reviews combining qualitative, quantitative, and/or mixed methods studies are increasingly popular because of their potential for addressing complex interventions and phenomena, specifically for assessing and improving clinical practice. A major challenge encountered with this type of review is the appraisal of the quality of individual studies given the heterogeneity of the study designs. The Mixed Methods Appraisal Tool (MMAT) was developed to help overcome this challenge. The aim of this study was to explore the usefulness of the MMAT by seeking the views and experiences of researchers who have used it. METHODS: We conducted a qualitative descriptive study using semistructured interviews with MMAT users. A purposeful sample was drawn from the researchers who had previously contacted the developer of the MMAT, and those who have published a systematic review for which they had used the MMAT. All interviews were transcribed verbatim and analyzed by 2 coders using thematic analysis. RESULTS: Twenty participants from 8 countries were interviewed. Thirteen themes were identified and grouped into the 2 dimensions of usefulness, ie, utility and usability. The themes related to utility concerned the coverage, completeness, flexibility, and other utilities of the tool. Those regarding usability were related to the learnability, efficiency, satisfaction, and errors that could be made due to difficulties understanding or selecting the items to appraise. CONCLUSIONS: On the basis of the results of this study, we make several recommendations for improving the MMAT. This will contribute to greater usefulness of the MMAT.
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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 | 0.746 | 0.908 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.011 | 0.016 |
| Bibliometrics | 0.053 | 0.041 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.028 | 0.017 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".