Advanced vaccinology training globally: Update and impact of the COVID-19 crisis
Bibliographic record
Abstract
The rapid development of innovations and new technologies, the focus on the life-course approach to immunization and equity, and the prevalent hesitancy towards vaccines requires immunization staff to be well-trained and updated regularly in order to deliver quality immunization services to the public. The need for advanced vaccinology training is therefore paramount. In preparation for a second Global Workshop on Advanced Vaccinology Training that took place in March 2022, this paper presents the results of a survey aiming to provide a thorough update of a landscape analysis on advanced vaccinology courses conducted in 2018 and a look at the impact of the COVID-19 crisis. Thirty-three course organizers responded to a survey to provide information on their respective course. Of those, 17 courses are short courses, 11 post-graduate courses and 5 are Master level courses. Most courses are organized on an annual basis. Even though some courses were not sustained overtime, the number of courses has been increasing during the last few years, and at least one vaccinology course is now being offered in each WHO region. Although the training capacity has increased tremendously, the need still exceeds the capacity and many courses have way more applicants than they can select. The most frequent challenges reported included sustainable funding and identifying faculty. The COVID-19 pandemic impacted the delivery of several vaccinology courses, which have been postponed or reformatted to an online or hybrid training event. An e-portal of the global collaboration has been established to facilitate communication between the different courses and to assist future course participants to identify the most suitable course for their needs.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".