Deep-seated psychological histories of COVID-19 vaccine hesitance and resistance
Bibliographic record
Abstract
To design effective pro-vaccination messaging, it is important to know "where people are coming from"-the personal experiences and long-standing values, motives, lifestyles, preferences, emotional tendencies, and information-processing capacities of people who end up resistant or hesitant toward vaccination. We used prospective data from a 5-decade cohort study spanning childhood to midlife to construct comprehensive early-life psychological histories of groups who differed in their vaccine intentions in months just before COVID vaccines became available in their country. Vaccine-resistant and vaccine-hesitant participants had histories of adverse childhood experiences that foster mistrust, longstanding mental-health problems that foster misinterpretation of messaging, and early-emerging personality traits including tendencies toward extreme negative emotions, shutting down mentally under stress, nonconformism, and fatalism about health. Many vaccine-resistant and -hesitant participants had cognitive difficulties in comprehending health information. Findings held after control for socioeconomic origins. Vaccine intentions are not short-term isolated misunderstandings. They are part of a person's style of interpreting information and making decisions that is laid down before secondary school age. Findings suggest ways to tailor vaccine messaging for hesitant and resistant groups. To prepare for future pandemics, education about viruses and vaccines before or during secondary schooling could reduce citizens' level of uncertainty during a pandemic, and provide people with pre-existing knowledge frameworks that prevent extreme emotional distress reactions and enhance receptivity to health messages. Enhanced medical technology and economic resilience are important for pandemic preparedness, but a prepared public who understands the need to mask, social distance, and vaccinate will also be important.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".