Programa global de trenamento em saúde: uma oportunidade para abrir a mente
Bibliographic record
Abstract
I belong to the Canadian Institutes of Health Research (CIHR) International Infectious Disease and Global Health Training Program (IID & GHTP). These are the program’s objectives (taken from: http://www.iidandghtp.com/iidghtp_program_objectives.html): 1) to equip trainees with the research, scientific knowledge, and skills to become outstanding researchers in infectious diseases and global health; 2) to create a novel and stimulating multidisciplinary and truly international research training environment that fosters creativity, opportunity, and innovation, and one that demands excellence; 3) to harness the unique opportunity offered by the critical mass of infectious diseases and global health infrastructure, research opportunities and outstanding scientists in the training of the next generation of infectious disease researchers; 4) to make available collaborative international research sites for the trainees’ primary research projects, sites for research practica and major course offerings; 5) to offer a shared learning environment, where trainees and mentors from all four of CIHR’s research pillars (clinical, social, basic, and epidemiology) and the four international training sites (Canada, Colombia, India, and Kenya) work cooperatively to explore issues of international infectious diseases and global health.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".