Enhancing Rigor in Quantitative Meta-Analyses for Mindfulness Research: A Comprehensive Guide
Bibliographic record
Abstract
Abstract Meta-analyses are considered the highest level of evidence in research design, providing a quantitatively estimated effect size for an outcome of interest pooled from some or all primary studies on a particular topic. The usefulness and accuracy of a meta-analysis depend on the rigor of the included studies and the methods adopted by the meta-analyst. In recent years, the number of meta-analyses in the field of mindfulness has increased, but the relative rigor of meta-analyses on mindfulness research has varied. Our aim with this report, therefore, is to provide a guide for future authors of mindfulness meta-analyses that offers recommendations and explanations for best practices in meta-analysis. We selected high-quality literature on meta-analyses and used the 19 meta-analyses published in the journal Mindfulness between January 2022 and 2024 to highlight methodological approaches that can enhance the rigor of future meta-analyses on mindfulness research. Although instructive content already exists on meta-analytic techniques, in this work, we brought together meta-analytic recommendations specific to mindfulness studies. We reviewed current literature on meta-analytics and now present recommendations for meta-analytic methods, reporting of meta-analytic results, and what to include in discussion sections. The present article also provides an overall checklist of mandatory and recommended items to be included in a meta-analysis.
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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 | 0.546 | 0.773 |
| Meta-epidemiology (narrow) | 0.010 | 0.012 |
| Meta-epidemiology (broad) | 0.018 | 0.029 |
| Bibliometrics | 0.048 | 0.029 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.016 | 0.014 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".