Interventions to improve adherence to reporting guidelines in health research: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: There is evidence that the use of some reporting guidelines, such as the Consolidated Standards for Reporting Trials, is associated with improved completeness of reporting in health research. However, the current levels of adherence to reporting guidelines are suboptimal. Over the last few years, several actions aiming to improve compliance with reporting guidelines have been taken and proposed. We will conduct a scoping review of interventions to improve adherence to reporting guidelines in health research that have been evaluated or suggested, in order to inform future interventions. METHODS AND ANALYSIS: Our review will follow the Joanna Briggs Institute scoping review methods manual. We will search for relevant studies in MEDLINE, EMBASE and Cochrane Library databases. Moreover, we will carry out lateral searches from the reference lists of the included studies, as well as from the lists of articles citing the included ones. One reviewer will screen the full list, which will be randomly split into two halves and independently screened by the other two reviewers. Two reviewers will perform data extraction independently. Discrepancies will be solved through discussion. In addition, this search strategy will be supplemented by a grey literature search. The interventions found will be classified as assessed or suggested, as well as according to different criteria, in relation to their target (journal policies, journal editors, authors, reviewers, funders, ethical boards or others) or the research stage at which they are performed (design, conducting, reporting or peer review). Descriptive statistical analysis will be performed. ETHICS AND DISSEMINATION: A paper summarising the findings from this review will be published in a peer-reviewed journal. This scoping review will contribute to a better understanding and a broader perspective on how the problem of adhering better to reporting guidelines has been tackled so far. This could be a major first step towards developing future strategies to improve compliance with reporting guidelines in health 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 | 0.272 | 0.219 |
| Meta-epidemiology (narrow) | 0.007 | 0.009 |
| Meta-epidemiology (broad) | 0.014 | 0.016 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.082 | 0.034 |
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".