‘It is surely a great criticism of our profession…’ The next 20 years of equity-focused systematic reviews
Bibliographic record
Abstract
The Cochrane Collaboration has been celebrating 20 years of its existence throughout 2013. For 20 years it has aimed to support policymakers, practitioners and patients in making better-informed decisions about healthcare and public health. Founded in 1993, it remains the largest global network of scientists, researchers, health policymakers and consumer advocates involved in the production of systematic reviews of healthcare evidence. For those involved in public health decision making, health equity continues to be a pivotal concern. Systematic reviews like those produced by Cochrane help identify potentially effective interventions, as well as identifying interventions that risk increasing inequity as an unintended consequence.1 ,2 Much relevant evidence on social determinants of health inequity now derives from systematic reviews, though in general there is not much of an equity perspective in clinical and public health research, although this is changing. The Cochrane and Campbell Equity Methods Group was set up to address this gap as well as to develop methods and improve reporting. This group has led initiatives to systematically consider equity in priority setting3 ,4 and to define personal and population characteristics across which equity might be important using the PROGRESS framework (Place of residence, Race/ethnicity/language/culture; Occupation, Gender/sex, Religion, Occupation, Socioeconomic status and social capital).5 However, these are initial steps and there are many remaining priorities for the next 20 years. On the equity front there …
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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 | 0.094 | 0.334 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.027 | 0.035 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".