Personal attitudes and denialist views about the COVID-19 pandemic in Italy: a national survey
Bibliographic record
Abstract
Since COVID-19 began to spread, hypotheses about the possible causes of the disease and its treatment have increased worldwide, engenedering fears and concerns. This context of uncertainty, as well as the great changes that people were forced to accept in their daily lives, have challenged the general population, affecting public opinion and collective imagination inevitably, with also a negative impact on compliance with public health policies. This study explored the personal attitudes towards the COVID-19 pandemic and their association with denial stances in the Italian context. The aim was to address the relevance of these phenomena and in what guise they are present in relation to the grounds supporting them, as an avenue to be more effective in public health under different domains. An online questionnaires was set out to survey the general population over 18 throughout the Italian country, including students and health professionals, to offer geographic and professional diversity. General population was also stratified based on their direct or indirect experience of COVID-19, whilst health participants were recruited with regard to their involvement in a COVID centre. A total of 2110 questionnaire were filled out between December 2020 and April 2021. Of the participants, 85.45% completely disagree with the possibility that COVID-19 is not real and that the cultural, social and economic system wanted us to believe otherwise, whereas 69% had doubts about what has been claimed to date about the existence of COVID-19. Trust in institutions and types of COVID-19 experience affected these beliefs. The results also show that stress, anxiety, sadness, and vulnerability increased as compared to the pre-COVID- 19 pandemic timeframe. The fundings of this national survey revealed how much behaviors based on social responsibility and rational prudence are important for defensing human life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".