Reporting guidelines for music-based interventions: An update and validation study
Bibliographic record
Abstract
Background: were developed to improve transparency and reporting quality of published research; however, problems with reporting quality persist. Methods: The purpose of this study was to update and validate the 2011 reporting guidelines using rigorous Delphi approach that involved an interdisciplinary group of MBI researchers; and to develop an explanation and elaboration guidance statement to support dissemination and usage. We followed the methodological framework for developing reporting guidelines recommended by the EQUATOR Network and guidance recommendations for developing health research reporting guidelines. Our three-stage process included: (1) an initial field scan, (2) a consensus process using Delphi surveys (two rounds) and Expert Panel meetings, and (3) development and dissemination of an explanation and elaboration document. Results: First-Round survey findings revealed that the original checklist items were capturing content that investigators deemed essential to MBI reporting; however, it also revealed problems with item wording and terminology. Subsequent Expert Panel meetings and the Second-Round survey centered on reaching consensus for item language. The revised RG-MBI checklist has a total of 12-items that pertain to eight different components of MBI interventions including name, theory/scientific rationale, content, interventionist, individual/group, setting, delivery schedule, and treatment fidelity. Conclusion: We recommend that authors, journal editors, and reviewers use the RG-MBI guidelines, in conjunction with methods-based guidelines (e.g., CONSORT) to accelerate and improve the scientific rigor of MBI research.
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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 | 0.624 | 0.763 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".