Empowering the future of evidence‐based healthcare: The Cochrane Early Career Professionals Network
Bibliographic record
Abstract
The Cochrane Early Career Professionals Network (ECPN) is a diverse global network of emerging volunteer health professionals committed to advancing evidence-based healthcare worldwide [1].Established in September 2019 by Robin Vernooij and Chris Champion, the ECPN was inspired by similar early career researcher groups in scientific associations.Cochrane received many inquiries from young researchers eager to engage with the activities, highlighting a growing demand for such a network.Cochrane's contributors come from varied backgrounds and cultures, including researchers, health sciences students, language translators, and other volunteers, all united in their commitment to supporting global healthcare initiatives [2].In line with Cochrane's mission to highlight the contributions of the next generation, Cochrane launched the "30 Under 30" initiative, inviting 30 young researchers with diverse professional backgrounds such as nurses, doctors, dentists, physiotherapists, biologists, psychologists, and journalists to join the series and tell their Cochrane Story.To build on the success of this initiative, Vernooij proposed formalizing this group into a network, which was well received as the idea aligned perfectly with the Cochrane membership strategy.Consequently, all participants in the "30 Under 30" series were invited to become the first members of the ECPN [2].The ECPN was due to be launched in Santiago, Chile at the 2019 Cochrane Colloquium, themed "Embracing Diversity," but due to the cancellation of the event, it was ultimately launched online later in 2019.The ECPN now plays a crucial role in promoting professional development, international networking, collaboration, and leadership within the Cochrane community, ensuring that the voices of early career professionals (ECP) are heard, and their potential maximized [1].The objective of this commentary is to provide a comprehensive overview of the ECPN in Cochrane, with the aim of raising awareness and enhancing the visibility of the network within the Cochrane and scientific community.
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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 | 0.055 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".