Conspiracies and contagion: COVID-19 related beliefs and associated mental health symptomatology
Bibliographic record
Abstract
The COVID-19 pandemic brought about unique challenges, leading to a simultaneous decline in global mental well-being and an increase in perceived social threats. The present study explores the interplay between COVID-19 beliefs and mental health symptoms in a multinational sample of 1500 individuals primarily from Canada, the US and Mexico. Between May 2020 and February 2021, participants completed an online survey assessing somatic symptoms, anxiety, depression, alexithymia and psychotic-like symptoms, along with the newly developed COVID-19 beliefs questionnaire (CBQ). The CBQ consisted of a series of statements corresponding to different beliefs about the origins and effects of the virus and it revealed two dimensions through Exploratory Factor Analysis: Fear of contagion of COVID-19 and COVID-19 denial/conspiratorial ideation.Correlation analyses and linear regressions revealed a negative correlation between these two belief patterns as well their distinct associations with mental health symptoms. Fear of contagion was positively predicted by somatic symptoms and anxiety. In contrast, COVID-19 denial/conspiratorial ideation was positively predicted by positive psychotic-like experiences, alexithymia, and depression, and negatively predicted by negative psychotic-like symptoms. Furthermore, the relationship between positive psychotic-like symptoms and CI was mediated negatively by negative psychotic-like symptoms, suggesting that individuals with higher self-reported delusional ideation and bizarre experiences but lower avolition during the pandemic were the most likely to endorse COVID-19 related conspiracy theories. We provide an interpretation of these results according to which these two profiles represent distinct doxastic threat responses, shaped by the interaction between the non-specific pandemic distress response and individual proneness to mental health symptomatology.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".