Vaccination behavior under uncertainty: A longitudinal study on factors associated with COVID-19 vaccination behavior in Japan with focus on the effect of close others’ vaccination behavior
Bibliographic record
Abstract
Experiencing apprehension and uncertainty toward the newly developed COVID-19 vaccines may be natural, but having people receive the vaccination is crucial in managing the pandemic. The present study aimed to examine how the beliefs, attitudes, and how the change in close others’ vaccination behavior from the beginning of the vaccine distribution predict the COVID-19 vaccine behavior today among Japanese sample. We conducted a longitudinal web-based survey at three time points between May 14-16, 2021 and August 12-18, 2022. At time 3, there were 1046 participants (mean age = 48.81, SD = 14.07, range = 20-80). 73% of the participants had received three or more shots at time 3, and when conducting ordinal logistic regression with intercept and slope of “Do you know anyone close to you who got vaccinated for COVID-19?” and intercept and slope of “Do you know anyone close to you who is saying they will not get vaccinated for COVID-19?,” COVID-19 vaccine uncertainty, COVID-19 risk perception, social norm of COVID-19 vaccination, COVID-19 vaccine attitude, gender, age, health status, impact of COVID-19 on life, experiences of testing positive for COVID-19, general vaccine attitude, knowledge of COVID-19 and big-five personality factors, the two intercepts were the two crucial factors in predicting later vaccination behaviors in relation with other factors. The result indicates that regardless of what attitudes people have toward vaccines, the initial status of the close others’ behavior is related relatively strongly to individuals’ vaccination behavior one and three months later.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".