Reported community engagement in health equity research published in high-impact medical journals: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To assess reported community engagement in the design and conduct of health equity-focused articles published in high-impact journals. DESIGN: Scoping review follows guidance from the Joanna Briggs Institute and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. DATA SOURCES: We selected the three highest-ranked journals from the 'Medicine-General and Internal' category including the Journal of the American Medical Association (JAMA), The Lancet and The New England Journal of Medicine (NEJM) along with all journals under their family of subspecialty journals (JAMA Network, The Lancet Group and the NEJM Group). Ovid MEDLINE was searched between 1 January 2021 to 22 September 2022. ELIGIBILITY CRITERIA: We included health equity-focused articles and assessed for the reporting of community engagement at each stage of the research process. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers extracted data from articles that met the inclusionary criteria. Inter-rater reliability was assessed using Cohen's kappa to measure the agreement between two independent reviewers. Disagreements were adjudicated by a third independent reviewer. RESULTS: 7616 articles were screened, 626 (8.2%) met our inclusion criteria: 457 (3.8%) were published by the JAMA Network; 167 (2.4%) by The Lancet Group; and 2 (0.2%) by the NEJM group. Most articles were from USA (68.4%) and focused on adult populations (57.7%). The majority of the articles focused on the topic of race/ethnicity (n=176, 28.1%), socioeconomic status (n=114, 18.2%) or multiple equity topics (n=111, 17.7%). The use of community engagement approaches was reported in 97 (15.5%) articles, of which 13 articles (13.4%) reported engagement at all stages. The most common form of reported engagement was in the acknowledgement or additional contribution section (n=86, 88.7%). CONCLUSIONS: Community engagement is infrequently reported in health equity-focused research published in high-impact medical journals.
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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 | 0.235 | 0.573 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.061 | 0.051 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".