Vaccination anxiety when vaccinations are available: The role of existential concerns
Bibliographic record
Abstract
This study examined how existential fears are related to COVID-19 vaccination anxiety and followed the Terror Management Theory (TMT) by examining the contribution of two existential concerns, subjective nearness-to-death (SNtD) and death anxiety, to COVID-19 vaccination anxiety during the first month of COVID-19 vaccinations. Data were collected during January 2021, when Israel was in lockdown, from a convenience sample of 381 Jewish Israelis (M = 55.39, SD = 17.17). Participants completed questionnaires examining demographics, SNtD, death anxiety and COVID-19 vaccination anxiety. A hierarchical regression analysis examined the connections between these variables and COVID-19 vaccination anxiety while controlling for demographics and for receiving COVID-19 vaccinations. In line with the hypotheses, SNtD and death anxiety were each positively associated with COVID-19 vaccination anxiety, and death anxiety levels moderated the positive connection between SNtD and COVID-19 vaccination anxiety, as this association was not significant for individuals with low death anxiety. The findings of this study provide preliminary evidence concerning the role of death anxiety in moderating the effect that SNtD has on COVID-19 vaccination anxiety. These findings are in line with the TMT and justify further investigation and may be utilized in future research in order to address COVID-19 vaccination anxiety more effectively.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".