Undermining support for COVID-19 public health policies: an analysis of the impact of subversive conspiracy narratives on Czech General Practitioners
Bibliographic record
Abstract
BACKGROUND: Limited knowledge exists regarding the impact of COVID-19 conspiracy theories on the professional practice of general practitioners (GPs). This study aimed to identify the basic characteristics of GPs who endorse COVID-19 conspiracy beliefs and compare their level of support for COVID-19 health policies with GPs who do not believe in conspiracies. METHODS: Between January and February 2021, a representative online survey was conducted among 1163 GPs in the Czech Republic. The sample was designed to be representative of members of The Association of GPs of the Czech Republic. RESULTS: The survey revealed that nearly 14% of the GPs surveyed believed in one or more COVID-19 conspiracies. The average age of GPs who endorsed conspiracies was 58, which was higher than the rest of the sample (average age of 50). GPs who believed in conspiracies were less likely to support COVID-19 public health policies and therapy recommendations, including vaccination. Logistic and linear regression analyses indicated that doctors who believed in conspiracies were 2.62 times less likely to have received a COVID-19 vaccine. Mediation analysis showed that approximately one-quarter (23.21%) of the total effect of trust in government information on support for public health policies was indirectly mediated by the endorsement of COVID-19 conspiracy beliefs. CONCLUSIONS: The study findings suggest a concerning association between belief in COVID-19 conspiracies and a reduced level of support for public health policies among GPs. These results underscore the importance of incorporating the 'conspiracy agenda' into medical authorities' more effective public health communication strategies.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".