Public perceptions and behavioural responses to the first COVID-19 pandemic wave in Italy: results from the iCARE study
Bibliographic record
Abstract
BACKGROUND: Italy was the first European country to be affected by COVID-19. Considering that many countries are currently battling the second wave of the pandemic, understanding people's perceptions and responses to government policies remain critical for informing on-going mitigation strategies. We assessed attitudes towards COVID-19 policies, levels of adherence to preventive behaviours, and the association between COVID-19 related concerns and adherence levels. METHODS: We recruited a convenience sample of Italian individuals from an international cross-sectional survey (www.icarestudy.com) from 27 March to 5 May 2020. Multivariate regression models were used to test the association between concerns and the adoption of preventive measures. RESULTS: The survey included 1332 participants [female (68%), younger than 25 (57%)] that reported high awareness (over 96%) and perceived importance (88%) of policies. We observed varied levels of adherence to: hand-washing (96%), avoiding social gatherings (96%), self-isolation if suspected or COVID-19 positive (77%). Significantly lower adherence to self-isolation was reported by individuals with current employment. High levels of concerns regarding health of other individuals and country economy were reported. Only health concerns for others were significantly associated with higher adherence to hand-washing behaviour. CONCLUSIONS: In order to inform current/future government strategies, we provide insights about population's responses to the initial pandemic phase in Italy. Communication approaches should consider addressing people's concerns regarding the health of other individuals to motivate adherence to prevention measures. Provision of social and economic support is warranted to avoid unequal impacts of governmental policies and allow effective adherence to self-isolating measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".