Is health equity considered in systematic reviews of the cochrane musculoskeletal group?
Bibliographic record
Abstract
OBJECTIVE: To determine whether Cochrane Musculoskeletal Group (CMSG) systematic reviews and corresponding primary studies of rheumatoid arthritis interventions report and analyze the data needed to assess the effectiveness of interventions in reducing socioeconomic differences in health and/or improving the health of the poor. METHODS: We selected all CMSG reviews on rheumatoid arthritis published since issue 1, 2003. Fourteen reviews were identified; 147 of the 156 primary studies included in these reviews were obtained and assessed. We extracted data on whether the dimensions place of residence, race/ethnicity/culture, occupation, gender, religion, education, socioeconomic status, and social capital and networks (PROGRESS) were reported or analyzed, and whether any interventions were aimed at disadvantaged or low- and middle-income country populations. RESULTS: Among the dimensions of PROGRESS reported at baseline in 147 primary studies, gender (89%) was the most commonly reported, followed by education (25%) and race/ethnicity (18%). Less than 50% of the systematic reviews reported dimensions of PROGRESS even when they had been reported in the primary study. Of 147 primary studies, 6 (5%) were aimed specifically at disadvantaged populations; another 6 reported on effectiveness by at least 1 dimension of PROGRESS. CONCLUSION: Primary studies of interventions for rheumatoid arthritis generally reported few variables necessary to answer questions about health inequalities. Most CMSG systematic reviews failed to assess those variables even when described in the primary studies. The Cochrane Health Equity Field welcomes the opportunity to provide guidance to systematic review authors on incorporating equity considerations into their reviews.
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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 | 0.346 | 0.759 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.010 |
| Bibliometrics | 0.040 | 0.029 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".