Knowledge for knowledge translation: The role of the Cochrane Collaboration
Bibliographic record
Abstract
Knowledge-translation (KT) activities, including continuing education, should be informed by the totality of available research evidence. Systematic reviews are a generic methodology used to synthesize evidence from a broad range of research methods addressing different questions. Over the past decade, there has been a dramatic increase in the availability of systematic reviews that could support KT activities. However, the conduct of systematic reviews is technically challenging, and it is not surprising that the quality of available reviews is variable. In addition, unless attempts are made to update systematic reviews, they rapidly become out of date. The Cochrane Collaboration is a unique, worldwide, not-for-profit organization that aims to help people make well-informed decisions about all forms of health care by preparing, maintaining, and promoting the accessibility of systematic reviews of the effects of health care interventions. Globally, over 13, 000 consumers, clinicians, policymakers, and researchers are involved with The Cochrane Collaboration and have to date produced over 2, 500 systematic reviews that can be used to inform KT activities. The Cochrane Collaboration publishes its reviews quarterly in The Cochrane Library. Cochrane reviews have been used to develop a number of KT-derivative products for professionals, consumers, and policymakers. Whereas most Cochrane Review groups focus on specific clinical areas, the Cochrane Effective Practice and Organisation of Care Group undertakes reviews of interventions to improve health care delivery and health care systems, including reviews of different KT activities. We summarize the activities of The Cochrane Collaboration and how these can contribute to KT activities.
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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 | 0.219 | 0.563 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.008 |
| Bibliometrics | 0.040 | 0.042 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.036 | 0.028 |
| Open science | 0.015 | 0.019 |
| Research integrity | 0.025 | 0.030 |
| Insufficient payload (model declined to judge) | 0.057 | 0.036 |
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".