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Record W2886510933 · doi:10.2147/hiv.s150107

Output from the CIHR Canadian HIV Trials Network international postdoctoral fellowship for capacity building in HIV clinical trials

2018· article· en· W2886510933 on OpenAlexafffundabout
Lawrence Mbuagbaw, Amy L. Slogrove, Jacqueline Sas, John Kunda, Frederick Morfaw, Jackson Mukonzo, Wei Cao, Gisele Ngomba-Kadima, Moleen Zunza, Pierre Ongolo‐Zogo, Phillip Njotang Nana, Anne Cockcroft, Neil Andersson, Nelson K. Sewankambo, Mark F. Cotton, Taishen Li, Taryn Young, Joel Singer, Jean Pierre Routy, Colin J.D. Ross, Kyaw Thin, Lehana Thabane, Aslam H. Anis

Bibliographic record

VenueHIV/AIDS - Research and Palliative Care · 2018
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS Research and Interventions
Canadian institutionsHamilton Health SciencesPopulation Health Research InstituteMcGill University Health CentreUniversity of British Columbia HospitalHIV Legal NetworkMcMaster UniversityUniversity of British ColumbiaSt. Joseph’s Healthcare HamiltonMcGill UniversityImpact
FundersCanadian Institutes of Health Research
KeywordsCapacity buildingMandateHuman immunodeficiency virus (HIV)Medical educationMedicinePolitical scienceProductivityClinical trialFamily medicineEconomic growth

Abstract

fetched live from OpenAlex

As a response to the human immunodeficiency virus (HIV) epidemic and part of Canadian Institutes for Health Research's mandate to support international health research capacity building, the Canadian Institutes for Health Research Canadian HIV Trial Network (CTN) developed an international postdoctoral fellowship award under the CTN's Postdoctoral Fellowship Awards Program to support and train young HIV researchers in resource-limited settings. Since 2010, the fellowship has been awarded to eight fellows in Cameroon, China, Lesotho, South Africa, Uganda and Zambia. These fellows have conducted research on a wide variety of topics and have built a strong network of collaboration and scientific productivity, with 40 peer-reviewed publications produced by six fellows during their fellowships. They delivered two workshops at international conferences and have continued to secure funding for their research, using the fellowship as a stepping stone. The CTN has been successful in building local HIV research capacity and forming a strong network of like-minded junior low- and middle-income country researchers with high levels of research productivity. They have developed into mentors, supervisors and faculty members, who, in turn, build local capacity. The sustainability of this international fellowship award relies on the recognition of its strengths and the involvement of other stakeholders for additional resources.

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)0.024
metaresearch head score (Gemma)0.062
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.062
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.515
GPT teacher head0.551
Teacher spread0.036 · 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.

Study designNot applicable
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

Citations4
Published2018
Admission routes3
Has abstractyes

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