Barriers and strategies for recruiting participants who identify as racial minorities in musculoskeletal health research: a scoping review
Bibliographic record
Abstract
Objective: Visible minorities are disproportionately affected by musculoskeletal disorders (MSD) and other diseases; yet are largely underrepresented in health research. The purpose of this scoping review was to identify barriers and strategies associated with increasing recruitment of visible minorities in MSD research. Methods: Electronic databases (MEDLINE, EMBASE, CINAHL, and PsycInfo) were searched. Search strategies used terms related to the concepts of 'race/ethnicity', 'participation', 'research' and 'musculoskeletal'. All research designs were included. Two reviewers independently screened titles and abstracts, completed full-text reviews, and extracted data. Papers that did not focus on musculoskeletal research, include racial minorities, or focus on participation in research were excluded. Study characteristics (study location, design and methods; sample characteristics (size, age, sex and race); MSD of interest) as well as barriers and strategies to increasing participation of visible minorities in MSD research were extracted from each article and summarized in a table format. Results: Of the 4,282 articles identified, 28 met inclusion criteria and were included. The majority were conducted in the United States (27 articles). Of the included studies, the groups of visible minorities represented were Black (25 articles), Hispanic (14 articles), Asian (6 articles), Indigenous (3 articles), Middle Eastern (1 article), and Multiracial (1 article). The most commonly cited barriers to research participation were mistrust, logistical barriers (e.g., transportation, inaccessible study location, financial constraints), and lack of awareness or understanding of research. Strategies for increasing diversity were ensuring benefit of participants, recruiting through sites serving the community of interest, and addressing logistical barriers. Conclusion: Understanding the importance of diversity in MSD research, collaborating with communities of visible minorities, and addressing logistical barriers may be effective in reducing barriers to the participation of visible minorities in health research. This review presents strategies to aid researchers in increasing inclusion in MSD-related 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 | 0.095 | 0.293 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".