Learning From a Massive Open Online COVID-19 Vaccination Training Experience: Survey Study
Bibliographic record
Abstract
BACKGROUND: To prepare key stakeholders for the global COVID-19 vaccination rollout, the World Health Organization and partners developed online vaccination training packages. The online course was launched in December 2020 on the OpenWHO learning platform. This paper presents the findings of an evaluation of this course. OBJECTIVE: The aim of this evaluation was to provide insights into user experiences and challenges, measure the impact of the course in terms of knowledge gained, and anticipate potential interest in future online vaccination courses. METHODS: The primary source of data was the anonymized information on course participants, enrollment, completion, and scores from the OpenWHO platform's statistical data and metric reporting system. Data from the OpenWHO platform were analyzed from the opening of the courses in mid-December 2020 to mid-April 2021. In addition, a learner feedback survey was sent by email to all course participants to complete within a 3-week period (March 19 to April 9, 2021). The survey was designed to determine the perceived strengths and weaknesses of the training packages and to understand barriers to access. RESULTS: During the study period, 53,593 learners enrolled in the course. Of them, 30,034 (56.0%) completed the course, which is substantially higher than the industry benchmark of 5%-10% for a massive open online course (MOOC). Overall, learners averaged 76.5% on the prequiz compared to 85% on the postquiz, resulting in an increase in average score of 9%. A total of 2019 learners from the course participated in the survey. Nearly 98% (n=1647 fully agree, n=308 somewhat agree; N=1986 survey respondents excluding missing values) of respondents fully or somewhat agreed that they had more confidence in their ability to support COVID-19 vaccination following completion of this course. CONCLUSIONS: The online vaccine training was well received by the target audience, with a measurable impact on knowledge gained. The key benefits of online training were the convenience, self-paced nature, access to downloadable material, and ability to replay material, as well as an increased ability to concentrate. Online training was identified as a timely, cost-effective way of delivering essential training to a large number of people to prepare for the COVID-19 vaccination rollout.
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 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".