Effects of Priming on Self-Reported COVID-19 Vaccination Intention
Bibliographic record
Abstract
This study was designed to examine the effects of priming on vaccination intention. Participants were first primed with global COVID-19 data and then with specifically tailored vaccination information: “No information,” “benefit only,” “balanced benefits and risks,” or “risks only.” We hypothesized that participants in the benefit vaccine information group, (n = 16) and the balanced vaccine information group (n = 16) would show increased intention to receive the COVID-19 vaccine compared to the vaccine risk information group (n = 17) and the control, no vaccine information group (n = 16). Further, that the risk information group would have a decreased intention to receive the vaccine compared with the control group. A general COVID-19 knowledge and experience survey was completed after presentation of the vaccination information and vaccination intention measure. The results from the one-way ANOVA did not show any statistically significant differences (p = .136). However, a medium effect size was detected (η2 = .08). Tukey’s test results show no statistically significant differences between the groups. Medium effects sizes were detected which may indicate that something was happening between the groups, but our study did not have enough power to detect it.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".