Evidence map›Paper›PMID 39103377›Full record

ArticleScientific reports2024

A latent class analysis of biosecurity attitudes and decision-making strategies of swine producers in the United States.

Richmond Silvanus Baye, Asim Zia, Scott C Merrill, Eric M Clark, Julia M Smith, Christopher Koliba

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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Richmond Silvanus BayeDepartment of Community Development and Applied Economics, University of Vermont, Burlington, VT, USA. rbaye@uvm.edu.
Asim ZiaDepartment of Community Development and Applied Economics, University of Vermont, Burlington, VT, USA.
Scott C MerrillSocial-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, USA.
Eric M ClarkSocial-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, USA.
Julia M SmithSocial-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, USA.
Christopher KolibaSocial-Ecological Gaming and Simulation Lab, University of Vermont, Burlington, VT, USA.

Funding

National Institute of Food and Agriculture 2021-67015-35236
6 · The paper itself

Abstract

The 2018 African swine fever (ASF) outbreak highlighted the importance of biosecurity in food production systems. Despite the significant economic impacts, the sociopsychological consequences on decision-making have been overlooked. Previous studies have focused on algebraic models and simulation-based models without considering the complex psychological and social factors that influence farmers' biosecurity behaviors and decision-making processes. This study aims to classify livestock producers into distinct subgroups based on their attitudes towards biosecurity. We conducted a survey presenting producers with three scenarios to assess their willingness to report suspected ASF cases, trust in government agencies, risk perception, biosecurity knowledge, willingness to purchase livestock insurance, motivation to invest in biosecurity, readiness to report suspected infections, and intention to contact a veterinarian. Using latent class analysis, we identified three distinct classes: Biosecurity Sceptics, Biosecurity Compliant, and Biosecurity Ultra-Compliant. Our results show that producer characteristics significantly influence biosecurity attitudes and class membership, with small-scale producers less likely to adopt ultra-compliant biosecurity practices. Attending at least one eradication program encouraged biosecurity compliance. This research informs the design of targeted food policy and risk communication strategies that account for attitudes of livestock producers to encourage biosecurity adoption and reduce the likelihood of Tier 1 disease incursion.

Indexed as

Decision MakingFarmersAfrican Swine FeverAnimal HusbandryAnimalsAttitudeBiosecurityFemaleHealth Knowledge, Attitudes, PracticeHumansMaleSurveys and QuestionnairesSwineUnited StatesAfrican swine feverBiosecurity behaviorLatent class analysis

Identifiers

PMID39103377
PMCPMC11300889

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.