Focus areas and methodological characteristics of North American-based health disparity research in sports medicine: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Health disparities are widely prevalent; however, little has been done to examine and address their causes and effects in sports and exercise medicine (SEM). We aimed to summarise the focus areas and methodology used for existing North American health disparity research in SEM and to identify gaps in the evidence base. DESIGN: Scoping review. DATA SOURCES: Systematic literature search of PubMed, Scopus, SPORTDiscus, CINAHL Plus with Full Text, Web of Science Core Collection and Cochrane Central Register of Controlled Trials. ELIGIBILITY CRITERIA: Full-text, peer-reviewed manuscripts of primary research, conducted in North America; published in the year 2000 or after, in English; and focusing on organised sports were included. RESULTS: 103 articles met inclusion criteria. Articles were classified into five focus areas: access to and participation in sports (n=45), access to SEM care (n=28), health-related outcomes in SEM (n=24), provider representation in SEM (n=5) and methodology (n=1). Race/ethnicity (n=39), socioeconomic status (n=28) and sex (n=27) were the most studied potential causes of health disparities, whereas sexual orientation (n=5), location (rural/urban/suburban, n=5), education level (n=5), body composition (n=5), gender identity (n=4) and language (n=2) were the least studied. Most articles (n=74) were cross-sectional, conducted on youth (n=55) and originated in the USA (n=90). CONCLUSION: Health disparity research relevant to SEM in North America is limited. The overall volume and breadth of research required to identify patterns in a heterogeneous sports landscape, which can then be used to inform positive change, need expansion. Intentional research focused on assessing the intersectionality, causes and consequences of health disparities in SEM is necessary.
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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 | 0.164 | 0.445 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.050 | 0.046 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".