Development of a Safety Decision-Making Scenario to Measure Worker Safety in Agriculture

doi:10.13031/jash.20.10358

Cited by 4 publications

(9 citation statements)

References 42 publications

(96 reference statements)

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Section: Decision-makingmentioning

confidence: 99%

“…Workers must make decisions based on their safety knowledge and external pressures (Mosher et al, 2014), yet various additional factors play a role in decision-making. Mosher et al (2014) presented adult grain elevator workers with a grain bin entry scenario and asked them to choose an action. The study found that safety was the main factor in worker decisions, whereas productivity, peer pressure, and supervisor opinion were less critical to the decision-making process (Mosher et al, 2014).…”

Section: Decision-makingmentioning

confidence: 99%

“…Mosher et al (2014) presented adult grain elevator workers with a grain bin entry scenario and asked them to choose an action. The study found that safety was the main factor in worker decisions, whereas productivity, peer pressure, and supervisor opinion were less critical to the decision-making process (Mosher et al, 2014).…”

Section: Decision-makingmentioning

confidence: 99%

“…Previous research acknowledged that youth work on family farms under parental supervision (Summers et al, 2017;Jinnah and Stoneman, 2016;Stoneman and Jinnah, 2016). This study seeks to determine if youth decision-making patterns concur with adult workers regarding grain bin entry, as documented by Mosher et al (2014). Data were collected in response to two research questions: 1.…”

Section: Decision-makingmentioning

confidence: 99%

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Factors that Influence Farm Safety Decisions of Young Adults when Entering Agricultural Grain Bins

Walls¹,

Mosher²

2021

Journal of Agricultural Safety and Health

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Highlights A grain handling scenario-based survey was administered to college students studying agriculture. Participants chose an action after reading each scenario and ranked factors affecting their decision-making. Most participants chose a “safe” option and claimed to value their personal safety when making decisions. Parental authority and pressure had little influence on participants’ decisions to enter grain bins. Abstract. The approaches that parents take in the supervision of youth who perform hazardous tasks on family farms can affect youth safety outcomes. This research examines the most significant factors affecting youths’ decisions to enter agricultural grain storage facilities. Over 200 students attending a Midwestern land-grant university who had grain bin experience as youth completed a decision-making survey. Students chose from a list of actions in three realistic but hypothetical scenarios involving grain bin entry. Afterward, they ranked factors according to the level of importance in their decision. Although most participants chose options that emphasized safety when answering the scenario questions and held the “personal safety” factor in highest regard, some chose higher-risk options and valued “productivity.” The findings revealed that youth held little value in their parents’ authority and pressure when making decisions related to grain bin entry. The study’s limitations are addressed, as are the implications of these findings for youth safety outcomes on family farms. Keywords: Decision-making, External pressures, Farm youth safety, Grain bin safety.

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Section: Decision-makingmentioning

confidence: 99%

Section: Decision-makingmentioning

confidence: 99%

Section: Decision-makingmentioning

confidence: 99%

Section: Decision-makingmentioning

confidence: 99%

See 2 more Smart Citations

Factors that Influence Farm Safety Decisions of Young Adults when Entering Agricultural Grain Bins

Walls¹,

Mosher²

2021

Journal of Agricultural Safety and Health

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“…Few studies have addressed safety issues in the grain handling industry. Mosher, Keren, Freeman, and Hurburgh [18] evaluated the role of human factors in the management of occupational safety in commercial grain handling facilities. Geng and Jepsen [11] conducted a survey on grain handling operations to evaluate how safety and health information was incorporated at the grain handling and storage facilities in Ohio, US.…”

Section: Introductionmentioning

confidence: 99%

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

2019

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The grain handling industry plays a significant role in U.S. agriculture by storing, distributing, and processing a variety of agricultural commodities. Commercial grain elevators are hazardous agro-manufacturing work environments where workers are prone to severe injuries, due to the nature of the activities and workplace. Safety incidents in agro-manufacturing operations generally arise from a combination of factors, rather than a single cause, therefore, research on occupational incidents must look deeper into identifying the underlying causes, through the application of advanced analyses methods. In occupational safety, it is possible to estimate and predict probability of safety risks through developing artificial neural network predictive models. Due to the significance of safety risk assessment in the design and prioritization of effective prevention measures, this study aimed at classifying and predicting causes of occupational incidents in grain elevator agro-manufacturing operations in the Midwest region of the United States. Workers’ compensation claims data, from 2008 to 2016, were utilized for training multilayer perceptron (MLP) and radial basis function (RBF) neural networks. Both MLP and RBF models could predict the probability of safety risks with a high overall accuracy of 60%, 61%. Based on values of AUC (area under the curve) from the ROC (receiving operating charts), both models predicted the probability of individual safety risks with a high accuracy rate of between 71.5% and 99.2%. In addition, sensitivity analysis showed that nature of injury is the most significant determinant of safety risks probability, along with type of injury. The novelty of this study is the use of the artificial neural network methodology to analyze multi-level causes of occupational incidents as the sources of safety risks in bulk storage facilities. The results confirm that artificial neural networks are useful in safety risk estimation, and identifying the incidents’ risk factors. The implementation of safety measures in grain elevators can help in preventing occupational injuries, saving lives, and reducing the occurrence and severity of such incidents in industrial work environments.

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Applied Machine Learning in Agro-Manufacturing Occupational Incidents

Kakhki

Freeman

Mosher

2020

Procedia Manufacturing

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Development of a Safety Decision-Making Scenario to Measure Worker Safety in Agriculture

Cited by 4 publications

References 42 publications

Factors that Influence Farm Safety Decisions of Young Adults when Entering Agricultural Grain Bins

Factors that Influence Farm Safety Decisions of Young Adults when Entering Agricultural Grain Bins

Use of Neural Networks to Identify Safety Prevention Priorities in Agro-Manufacturing Operations within Commercial Grain Elevators

Applied Machine Learning in Agro-Manufacturing Occupational Incidents

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