Consensus Recommendations for Antiracist Child Health Research: A Modified Delphi Study
Bibliographic record
Abstract
OBJECTIVE This study aimed to develop consensus on a comprehensive and abridged list of recommendations for conducting antiracist research across all stages of a pediatric research project. Antiracist research aims to ensure that the construct of race is correctly interpreted and that racially and ethnically minoritized communities are fairly engaged throughout a research project. METHODS Using a modified Delphi approach, experts in equitable pediatric research methods completed 3 rounds of surveys and virtual focus groups between April and December 2023. Round 1 asked experts to add to or revise themes and subthemes gathered from a systematic review of published antiracist practices. Round 2 included ranking items’ importance; items voted in the top 50% by 60% of experts were included in an abridged list of essential practices. In Round 3, experts reviewed the final guidance and had the option to rescue items for inclusion in the abridged guidance. RESULTS Fourteen experts with diverse personal and professional backgrounds participated. Experts added new themes and edited existing ones, creating a comprehensive list of 68 recommendations by consensus and identifying 36 to include in the abridged list of essential practices. They also discussed complex topics such as who these recommendations apply to, the nuances of equitable community engagement, and the dangers of health equity tourism. CONCLUSIONS An expert panel achieved consensus on a comprehensive and abridged list of recommendations for how to conduct research in an antiracist, equitable way. These can inform research teams, regulatory agencies, and funders to improve pediatric 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 | 0.436 | 0.389 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".