Strategies to Improve Equitable Access to Early Osteoarthritis Diagnosis and Management: An Updated Review
Bibliographic record
Abstract
Though osteoarthritis (OA) affects millions of people worldwide, many fail to access recommended early, person-centered OA care, particularly women who are disproportionately impacted by OA. A prior review identified few strategies to improve equitable access to early diagnosis and management for multiple disadvantaged groups. We aimed to update that review with literature published in 2010 or later on strategies to improve OA care for disadvantaged groups including women. We identified only 11 eligible studies, of which only 2 (18%) focused on women only. Other disadvantaged groups targeted in the largely US-based studies included patients who are Black, Spanish-speaking, rural, and adults aged 60 years and older. All studies evaluated interventions targeted to patients; 4 (36%) assessed video decision aids, and 7 (63.6%) assessed in-person, video, or telephone self-management education. Interventions were often multifaceted (n = 9, 82%), and most studies (n = 8, 73%) achieved positive outcomes in at least some outcomes measured. No studies evaluated clinician- or system-level strategies. Few studies (n = 5, 45%) described how they tailored strategies to disadvantaged groups or how they addressed person-centered care concepts apart from enabling self-management. Future research is needed to develop, implement, evaluate, and scale-up multilevel strategies to enhance equitable, person-centered OA care for disadvantaged groups including women.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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; 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".