Reporting guidelines for music-based interventions checklist: Explanation and elaboration guide
Bibliographic record
Abstract
Background: (RG-MBI, published 2011), multiple reviews reveal sustained problems with reporting quality and consistency. To address this, we convened an interdisciplinary expert panel to update and improve the utility and validity of the existing guidelines using a rigorous Delphi approach. The resulting updated checklist includes 12-items across eight areas considered essential to ensure transparent reporting of MBIs. Methods: The purpose of this explanation and elaboration document is to facilitate consistent understanding, use, and dissemination of the revised RG-MBI. Members of the interdisciplinary expert panel collaborated to create the resulting guidance statement. Results: This guidance statement offers: (1) the scope and intended use of the RG-MBI, (2) an explanation for each checklist item, with examples from published studies, and (3) two published studies with annotations indicating where the authors reported each checklist item. Conclusion: Broader uptake of the RG-MBIs by study authors, editors, and peer reviewers will lead to better reporting of MBI trials, and in turn facilitate greater replication of research, improve cross-study comparisons and meta-analyses, and increase implementation of findings.
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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 | 0.185 | 0.433 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.047 | 0.026 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".