Development of START-EDI guidelines for reporting equality, diversity and inclusion in research: a study protocol
Bibliographic record
Abstract
INTRODUCTION: Acknowledging equality, diversity and inclusion (EDI) in research is not only a moral imperative but also an important step in avoiding bias and ensuring generalisability of results. This protocol describes the development of STAndards for ReporTing EDI (START-EDI) in research, which will provide a set of minimum standards to help researchers improve their consistency, completeness and transparency in EDI reporting. We anticipate that these guidelines will benefit authors, reviewers, editors, funding organisations, healthcare providers, patients and the public. METHODS AND ANALYSIS: To create START-EDI reporting guidelines, the following five stages are proposed: (i) establish a diverse, multidisciplinary Steering Committee that will lead and coordinate guideline development; (ii) a systematic review to identify the essential principles and methodological approaches for EDI to generate preliminary checklist items; (iii) conduct an international Delphi process to reach a consensus on the checklist items; (iv) finalise the reporting guidelines and create a separate explanation and elaboration document; and (v) broad dissemination and implementation of START-EDI guidelines. We will work with patient and public involvement representatives and under-served groups in research throughout the project stages. ETHICS AND DISSEMINATION: The study has received ethical approval from the Imperial College London Research Ethics Committee (study ID: 7592283). The reporting guidelines will be published in open access peer-reviewed publications and presented in international conferences, and disseminated through community networks and forums. TRIAL REGISTRATION NUMBER: The project is pre-registered within the Open Science Framework (https://osf.io/8udbq/) and the Enhancing the Quality and Transparency of Health Research Network.
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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 | 0.431 | 0.549 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.065 | 0.040 |
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".