Output from the CIHR Canadian HIV Trials Network international postdoctoral fellowship for capacity building in HIV clinical trials
Bibliographic record
Abstract
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.
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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 | 0.056 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.117 | 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".