Advanced vaccinology education: Landscaping its growth and global footprint
Bibliographic record
Abstract
In preparation for the first Global Vaccinology Training workshop in 2018, a survey of 27 advanced vaccinology courses was conducted to provide a landscape of the vaccinology education around the world. Advanced vaccinology courses have expanded dramatically over the last 20 years, with courses located in almost all regions, but with underrepresentation amongst the Eastern part of the European region, the Eastern Mediterranean and the Western Pacific regions. Most courses are of short duration (<2 weeks), have a global or regional reach, and attract a diverse range of participants from high, middle and low-income countries with representation from public health, academia, industry and less often regulators. Lack of sustainable funding and time commitments of faculty and coordinators is a constraint for most vaccinology courses and needs to be addressed. Continuation and extension of training in vaccinology worldwide will be necessary as increasing number of new and more complex vaccines are introduced, vaccine safety concerns and rumors continue their trend, and reemergence of some vaccine-preventable diseases will require a competent workforce to advance and deploy immunizations to larger populations.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".