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Record W2527839975 · doi:10.3402/gha.v9.30434

‘Research clinics’: online journal clubs between south and north for student mentoring

2016· article· en· W2527839975 on OpenAlexaff
Salla Atkins, Dinansha Varshney, Elnta Meragia, Merrick Zwarenstein, Vishal Diwan

Bibliographic record

VenueGlobal Health Action · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsWestern University
Fundersnot available
KeywordsGlobeLow and middle income countriesMedical educationGlobal healthDeveloping countryCapacity buildingMedicinePublic relationsPolitical scienceNursingEconomic growthPublic health

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.370

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.211
GPT teacher head0.545
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2016
Admission routes1
Has abstractyes

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