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Introduction: A critical aspect of occupational safety is workplace inspections by experts, in which hazards are identified. Scientific research demonstrates that expectation generated by context (i.e., prior knowledge and experience) can bias the judgments of professionals and that individuals are largely unaware when their judgments are affected by bias.
Method: The current research tested the reliability and biasability of expert safety inspectors' judgments. We used a two-study design (Study 1, N = 83; Study 2, N = 70) to explore the potential of contextual, task-irrelevant, information to bias professionals' judgments. We examined three main issues: (1) the effect that biasing background information (safe and unsafe company history) had on professional regulatory safety inspectors' judgments of a worksite; (2) the reliability of those judgments amongst safety inspectors and (3) inspectors' awareness of bias in their judgments and confidence in their performance.
Results: Our findings establish that: (i) inspectors' judgments were biased by historical contextual information, (ii) they were not only biased, but the impact was implicit: they reported being unaware that it affected their judgments, and (iii) independent of our manipulations, inspectors were inconsistent with one another and the variations were not a product of experience.
Conclusion: Our results are a replication of findings from a host of other professional domains, where honest, hardworking professionals underappreciate the biasing effect of context on their decision making. The current paper situates these findings within the relevant research on safety inspection, cognitive bias and decision making, as well as provides suggestions for bias mitigation in workplace safety inspection. Practical Application: Our results have implications for occupational health and safety given that inspection is an integral aspect of an effective safety system. In addition to our findings, this study contributes to the literature by providing recommendations regarding how to mitigate the effect of bias in inspection.
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http://dx.doi.org/10.1016/j.jsr.2021.01.002 | DOI Listing |
Front Plant Sci
February 2025
College of Engineering, Shenyang Agricultural University, Shenyang, Liaoning, China.
Introduction: With the escalating demands for agricultural product quality in modern agriculture, peanuts, as a crucial economic crop, have their pod appearance quality directly influencing market value and consumer acceptance. Traditionally, the visual inspection of peanut pod appearance quality relies heavily on manual labor, which is not only labor-intensive and inefficient but also susceptible to subjective judgments from inspectors, thereby compromising the consistency and accuracy of inspection outcomes. Consequently, the development of a rapid, accurate, and automated inspection system holds significant importance for enhancing production efficiency and quality control in the peanut industry.
View Article and Find Full Text PDFPLoS One
May 2025
Engineering Research Center of Hydrogen Energy Equipment& Safety Detection, Universities of Shaanxi Province, Xijing University, Xi'an, China.
The traditional method of corn quality detection relies heavily on the subjective judgment of inspectors and suffers from a high error rate. To address these issues, this study employs the Swin Transformer as an enhanced base model, integrating machine vision and deep learning techniques for corn quality assessment. Initially, images of high-quality, moldy, and broken corn were collected.
View Article and Find Full Text PDFUltrasonics
July 2024
PULÉTS, École de technologie supérieure, 1100 Notre-Dame Ouest, Montréal QC H3C 1K3, Canada. Electronic address:
Phased array ultrasonic testing (PAUT) requires highly trained and qualified personnel to interpret and analyze images. It takes a solid understanding of wave propagation physics to comprehend the generated images. As such, the inspector's judgment and level of experience have a significant impact on the analysis's outcome.
View Article and Find Full Text PDFMar Pollut Bull
December 2023
Department of Maritime Transportation and Management Engineering, Istanbul Technical University, Tuzla 34940, Istanbul, Turkey.
Nanomaterials (Basel)
May 2023
School of Physics Science and Technology, Xinjiang University, Urumqi 830046, China.
In this study, chitin fibers (CFs) were combined with molybdenum sulfide (MoS) to develop high-performance sensors, and chitin carbon materials were innovatively introduced into the application of gas sensing. MoS/CFs composites were synthesized via a one-step hydrothermal method. The surface properties of the composites were greatly improved, and the fire resistance effect was remarkable compared with that of the chitin monomer.
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