COVID-19 conspiracy beliefs in Poland. Predictors, psychological and social impact and adherence to public health guidelines over one year
Bibliographic record
Abstract
This study examines demographic and attitudinal determinants of belief in COVID-19 conspiracy beliefs in Poland and their impact on psychological well-being, social functioning, and adherence to public health measures over one year. A cross-sectional study with a retrospective component was conducted one year after the pandemic outbreak (N = 1000). A COVID-19 conspiracy belief factor, extracted via PCA, served as the dependent variable in hierarchical regression models. Changes in P-score (psychological distress), S-score (social functioning), WHO-5 score (well-being), and adherence to public health guidance were analyzed using t-tests. Key predictors of conspiracy belief included lower education, younger age, higher religiosity, and distrust in experts. Conspiracy believers (CTB) exhibited significantly higher P-scores (greater psychological distress) compared to non-believers (N-CTB). While S-score (social functioning) and WHO-5 score (well-being) declined in both groups over time, differences between CTB and N-CTB were not significant. Stronger conspiracy beliefs were associated with lower adherence to public health guidelines from the pandemic's outset, with no significant improvement after one year. These findings confirm previous research linking conspiracy beliefs to reduced adherence to health measures and poorer psychological outcomes. However, they challenge assumptions that conspiracy beliefs necessarily impair well-being and social functioning over time. Strengthening institutional trust and addressing misinformation remain critical for improving public health compliance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".