An Online Tailored COVID-19 Vaccination Decision Aid for Dutch Citizens: Development, Dissemination, and Use
Bibliographic record
Abstract
BACKGROUND: Since December 2019, COVID-19 led to a pandemic causing many hospitalizations and deaths. Vaccinations were developed and introduced to control viral transmission. In the Dutch context, the decision to accept vaccination is not mandatory. An informed decision is based on sufficient and reliable information, in line with one's attitudes and values, and with consideration of pros and cons. To support people in informed decision-making, we developed an online COVID-19 vaccination decision aid (DA). OBJECTIVE: This article aims to describe the development, dissemination, and use of the DA. METHODS: Building on a previously developed DA, the COVID-19 vaccination DA was developed in 3 phases following a user-centered design approach: (1) definition phase, (2) concept testing, and (3) prototype testing. End users, individuals with low literacy, and experts (with relevant expertise on medical, behavioral, and low literacy aspects) were involved in the iterative development, design, and testing, with their feedback forming the basis for adaptations to the DA. RESULTS: The DA was developed within 14 weeks. The DA consists of 3 modules, namely, Provide Information, Support Decision-Making, and Facilitate Actions Following a Decision. These modules are translated into various information tiles and diverse functionalities such as a knowledge test, a value clarification tool using a decisional balance, and a communication tool. The DA was disseminated for use in May 2021. Users varied greatly regarding age, gender, and location in the Netherlands. CONCLUSIONS: This paper elaborates on the development of the COVID-19 vaccination DA in a brief period and its dissemination for use among Dutch adults in the Netherlands. The evaluation of use showed that we were able to reach a large proportion and variety of people throughout the Netherlands.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".