Reporting guidelines for music-based interventions checklist: Explanation and elaboration guide
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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