COVID-19: Belief in Conspiracy Theories and the Need for Quarantine
Bibliographic record
Abstract
Background. Situations that are characterized by unexpected scenarios, unpredictable developments, and risks to life and health facilitate beliefs in conspiracy theories. These beliefs — together with reliable information, intentional and unintentional misinformation and rumors — determine attitudes toward the situations and ways to overcome them. Objective. To examine the effect of belief in conspiracy theories on the recognition of the need for quarantine during the COVID-19 pandemic; the effect of personality traits on belief in conspiracy theories and on the recognition of the need for quarantine; the relationship of belief in conspiracy theories with assessment of the dangers of COVID-19 and with feelings of hopelessness. Design. The study was conducted over a period when the number of coronavirus cases was growing, during the first three weeks of the lockdown in Russia. The sample included 667 undergraduate and graduate students aged 16–31 (M = 20.44, SD = 2.38); 74.2% of the participants were women. Respondents filled out two online questionnaires. The first related to perceptions about the COVID-19 pandemic; the second was a brief HEXACO inventory. Results. Belief in Conspiracy Theories accounts for 13% of variance in Recognition of the Need for Quarantine; together with Dangers of COVID-19 and Hopelessness, conspiracy beliefs account for more than a quarter of the variance. Personality traits defined in the context of the 6-factor personality model have a small effect on conspiracy beliefs about the coronavirus and on perception of the need for quarantine. Conclusion. Belief in conspiracy theories is associated not only with irrational views of reality, but also with the adoption of ineffective behaviors.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".