Reporting of Determinants of Health Inequities in Rheumatoid Arthritis Randomized Controlled Trials in Canada: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: More than ever, it is important to consider inclusion and diversity in rheumatology research. We reviewed and synthesized randomized controlled trials (RCTs) for rheumatoid arthritis (RA) in Canada with the aim of characterizing participants and identifying how determinants of health inequities are reported. METHODS: We conducted a scoping review following the Arksey and O'Malley framework. We searched Medline (1990 to December 2021), Embase (1990 to December 2021), and CENTRAL (inception to December 2021) for articles meeting inclusion criteria of: 1) used an RCT design; 2) evaluated pharmacologic or nonpharmacologic interventions; 3) included participants with RA; and 4) conducted in Canada. Data extraction was guided by the Campbell and Cochrane Equity Methods Group's PROGRESS-Plus framework on determinants that lead to health inequities (e.g., place of residence; race; occupation; gender/sex; religion; education; socioeconomic status; and social capital). RESULTS: Of 6,290 unique records, 42 were eligible for inclusion. We grouped studies according to 3 time periods: before 2000; 2000-2010; and 2011 to present. Participants of included studies were mostly middle-aged, female, and White. Sex and age were the most widely reported determinants in 41 studies. Other determinants reported were race (15 studies), education (11 studies), socioeconomic status (7 studies), and occupation (6 studies). Religion, features of relationships, and time-dependent relationships were not reported in any study. CONCLUSION: This scoping review suggests limited reporting on determinants of health inequities in RCTs for RA in Canada. Establishing reporting standards for equity factors in RCTs is important for addressing health inequities and informing accessible research and care for patients with RA.
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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 | 0.240 | 0.607 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.012 |
| Bibliometrics | 0.030 | 0.039 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".