Evidence map›Paper›PMID 36337194›Full record

ArticleFrontiers in veterinary science2022

Assessing strategic, tactical, and operational decision-making and risk in a livestock production chain through experimental simulation platforms.

Christopher Koliba, Scott C Merrill, Asim Zia, Gabriela Bucini, Eric Clark, Trisha R Shrum, Serge Wiltshire, Julia M Smith

Abstract read
In one paragraph

Article in Frontiers in veterinary science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Christopher KolibaSocial Ecological Gaming and Simulation Lab, School of Public Administration and Affairs, University of Kansas, Lawrence, KS, United States.
Scott C MerrillSocial Ecological Gaming and Simulation (SEGS) Lab, Plant and Soil Science Department, Gund Institute for Environment, University of Vermont, Burlington, VT, United States.
Asim ZiaSocial Ecological Gaming and Simulation Lab, School of Public Administration and Affairs, University of Kansas, Lawrence, KS, United States.
Gabriela BuciniSocial Ecological Gaming and Simulation (SEGS) Lab, Plant and Soil Science Department, University of Vermont, Burlington, VT, United States.
Eric ClarkSocial Ecological Gaming and Simulation (SEGS) Lab, Plant and Soil Science Department, University of Vermont, Burlington, VT, United States.
Trisha R ShrumSocial Ecological Gaming and Simulation (SEGS) Lab, Community Development & Applied Economics Department, University of Vermont, Burlington, VT, United States.
Serge WiltshirePlant Biology Department, Food Systems Research Center, University of Vermont, Burlington, VT, United States.
Julia M SmithSocial Ecological Gaming and Simulation (SEGS) Lab, Animal and Veterinary Sciences Department, University of Vermont, Burlington, VT, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper provides a research summary of a series of serious games and simulations that form the basis of an experimental platform for the study of human decision-making and behavior associated with biosecurity across complex livestock production chains. This platform is the first of its kind to address the challenges associated with scaling micro-behavior of biosecurity decision-making to macro-patterns of disease spread across strategic, tactical and operational levels, capturing the roles that facility managers and front-line workers play in making biosecurity decisions under risk and uncertainty. Informational and incentive treatments are tested within each game and simulation. Behavioral theories are used to explain these findings. Results from serious games in the form of behavioral probability distributions are then used to simulate disease incidence and spread across a complex production chain, demonstrating how micro-level behaviors contribute to larger macro-level patterns. In the case of this study, the propensity to adopt micro-level biosecurity practices are applied to a network percolation disease spread model. By presenting the suite of companion models of behavior and disease spread we are able to capture scaling dynamics of complex systems, and in the process, better understand how individual behaviors impact whole systems.

Indexed as

biosecuritycompliancemicro-macronudgesrisk perceptionscaling

Identifiers

PMID36337194
PMCPMC9634728

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.