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Table_1_Risk Perception and Media in Shaping Protective Behaviors: Insights From the Early Phase of COVID-19 Italian Outbreak.pdf

2020· dataset· en· W6946059464 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2020
Typedataset
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Taxonomy and Phylogenetics
Canadian institutionsnot available
Fundersnot available
KeywordsPerceptionRelevance (law)Risk perceptionPandemicSocial distanceQuarter (Canadian coin)Social mediaContainment (computer programming)Phase (matter)

Abstract

fetched live from OpenAlex

<p>In the absence of target treatments or vaccination, the SARS-CoV-2 pandemic can be impeded by effectively implementing containment measures and behaviors. This relies on individuals’ adoption of protective behaviors, their perceived risk, and the use and trust of information sources. During a health emergency, receiving timely and accurate information enables individuals to take appropriate actions to protect themselves, shaping their risk perception. Italy was the first western country plagued by COVID-19 and one of the most affected in the early phase. During this period, we surveyed 2,223 Italians before the national lockdown. A quarter of the sample perceived COVID-19 less threatening than flu and would not vaccinate, if a vaccine was available. Besides, most people perceived containment measures, based on social distancing or wearing masks, not useful. This perceived utility was related to COVID-19 threat perception and efficacy beliefs. All these measures were associated with the use of media and their truthfulness: participants declared to mainly use the Internet, while health organizations’ websites were the most trusted. Although social networks were frequently used, they were rated lower for trustfulness. Our data differ from those obtained in other community samples, suggesting the relevance to explore changes across different countries and during the different phases of the pandemic. Understanding these phenomena, and how people access the media, may contribute to improve the efficacy of containment measures, tailoring specific policies and health communications.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.029
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0300.000

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.

Opus teacher head0.095
GPT teacher head0.273
Teacher spread0.178 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreDataset

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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