‘Research clinics’: online journal clubs between south and north for student mentoring
Bibliographic record
Abstract
BACKGROUND: Capacity development in health research is high on the agenda of many low- and middle-income countries. OBJECTIVE: The ARCADE projects, funded by the EU, have been working in Africa and Asia since 2011 in order to build postgraduate students' health research capacity. In this short communication, we describe one initiative in these projects, that of research clinics - online journal clubs connecting southern and northern students and experts. DESIGN: We describe the implementation of these research clinics together with student and participant experiences. RESULTS: From 2012 to 2015, a total of seven journal clubs were presented by students and junior researchers on topics related to global health. Sessions were connected through web conferencing, connecting experts and students from different countries. CONCLUSIONS: The research clinics succeeded in engaging young researchers across the globe and connecting them with global experts. The contacts and suggestions made were appreciated by students. This format has potential to contribute toward research capacity building in low- and middle-income countries.
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.076 | 0.019 |
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".