The role of psychological factors in workplace safety

Workplace safety researchers and practitioners generally agree that it is necessary to understand the psychological factors that influence people's workplace safety behaviour. Yet, the search for reliable individual differences regarding psychological factors associated with workplace safety has lead to sparse results and inconclusive findings. The aim of this study was to investigate whether there are differences between the psychological factors, cognitive ability, personality and work-wellness of employees involved in workplace incidents and accidents and/or driver vehicle accidents and those who are not. The study population (N = 279) consisted of employees employed at an electricity supply organisation in South Africa. Mann–Whitney U-test and one-way ANOVA were conducted to determine the differences in the respective psychological factors between the groups. These results showed that cognitive ability did not seem to play a role in workplace incident/accident involvement, including driver vehicle accidents, while the wellness factors burnout and sense of coherence, as well as certain personality traits, namely conscientiousness, pragmatic and gregariousness play a statistically significant role in individuals' involvement in workplace incidents/accidents/driver vehicle accidents. Safety practitioners, managers and human resource specialists should take cognisance of the role of specifically work-wellness in workplace safety behaviour, as management can influence these negative states that are often caused by continuously stressful situations, and subsequently enhance work place safety.     

Comparison of fatal occupational injuries in construction industry in the United States,South Korea,and China

In order to understand country specific similarities and differences in fatality risks of construction industry, this study compared the profile of fatal occupational injuries (FOI) in construction industry in three countries. Occupational fatal injury data of U.S., South Korea, and China from 2011 to 2015 were obtained from various public resources and analyzed with statistical analyses. Results showed that the construction industry in all three countries had consistently high FOI and the top common accident types were “fall from a higher level” and “struck by”. China recorded the highest average number of FOI in construction of 2328, followed by the U.S. of 881 and South Korea of 533. However, South Korea had the highest average mortality rate of 17.9, followed by the U.S. of 9.4 and China of 5.3. In addition, Poisson regression indicated that the number of FOI of the U.S. increased at an annual rate of 5.8%, whereas China's decreased at 7.1% and South Korea's decreased at 4.9%. The similarities and differences between U.S. and South Korea in workforce profile of FOI were also reported. However, the findings should be interpreted with caution due to probable underreporting of FOI and differences in surveillance systems.     

Warning: statistical benchmarking is addictive. Kicking the habit in machine learning

Algorithm performance evaluation is so entrenched in the machine learning community that one could call it an addiction. Like most addictions, it is harmful and very difficult to give up. It is harmful because it has serious limitations. Yet, we have great faith in practicing it in a ritualistic manner: we follow a fixed set of rules telling us the measure, the data sets and the statistical test to use. When we read a paper, even as reviewers, we are not sufficiently critical of results that follow these rules. Here, we will debate what are the limitations and how to best address them. This article may not cure the addiction but hopefully it will be a good first step along that road.     

Compensation for occupational injuries and diseases: its effect upon prevention at the workplace

Abstract

In Switzerland, as in many other industrialized countries, the nature and extent of prevention at the workplace is determined, at least partially, by known cases of compensated occupational injuries and diseases. At both the national and international levels ∥ILO conventions) injuries and diseases that fit appropriate lists and definitions are eligible for compensation. It has been found, based upon an investigation of a representative sample (965 subjects) of the working population in the French-speaking region of Switzerland, that this restrictive view does not take into account the fact that a large proportion of injuries and diseases are claimed by the victims to be caused by their job. These injuries and diseases, responsible for at least one month's absence from work, are not considered to be eligible for compensation but must be covered by the patient's own insurance. Moreover, the survey showed that workers considered the ill effects on health and safety to be a consequence less of the physical working environment than of the work organization, and that this category of risks was not recognized. Thus, in addition to the reduction of hazards by the application of industrial hygiene, an informed improvement of the workplace and the work organization was required. Consequently, laws and regulations on occupational injuries and diseases should be changed in order to emphasize the role of more appropriate preventive tools, which includes ergonomics.     

Crowdsourced reliable labeling of safety-rule violations on images of complex construction scenes for advanced vision-based workplace safety

Construction workplace hazard detection requires engineers to analyze scenes manually against many safety rules, which is time-consuming, labor-intensive, and error-prone. Computer vision algorithms are yet to achieve reliable discrimination of anomalous and benign object relations underpinning safety violation detections. Recently developed deep learning-based computer vision algorithms need tens of thousands of images, including labels of the safety rules violated, in order to train deep-learning networks for acquiring spatiotemporal reasoning capacity in complex workplaces. Such training processes need human experts to label images and indicate whether the relationship between the worker, resource, and equipment in the scenes violate spatiotemporal arrangement rules for safe and productive operations. False alarms in those manual labels (labeling no-violation images as having violations) can significantly mislead the machine learning process and result in computer vision models that produce inaccurate hazard detections. Compared with false alarms, another type of mislabels, false negatives (labeling images having violations as “no violations”), seem to have fewer impacts on the reliability of the trained computer vision models.This paper examines a new crowdsourcing approach that achieves above 95% accuracy in labeling images of complex construction scenes having safety-rule violations, with a focus on minimizing false alarms while keeping acceptable rates of false negatives. The development and testing of this new crowdsourcing approach examine two fundamental questions: (1) How to characterize the impacts of a short safety-rule training process on the labeling accuracy of non-professional image annotators? And (2) How to properly aggregate the image labels contributed by ordinary people to filter out false alarms while keeping an acceptable false negative rate? In designing short training sessions for online image annotators, the research team split a large number of safety rules into smaller sets of six. An online image annotator learns six safety rules randomly assigned to him or her, and then labels workplace images as “no violation” or ‘violation” of certain rules among the six learned by him or her. About one hundred and twenty anonymous image annotators participated in the data collection. Finally, a Bayesian-network-based crowd consensus model aggregated these labels from annotators to obtain safety-rule violation labeling results. Experiment results show that the proposed model can achieve close to 0% false alarm rates while keeping the false negative rate below 10%. Such image labeling performance outdoes existing crowdsourcing approaches that use majority votes for aggregating crowdsourced labels. Given these findings, the presented crowdsourcing approach sheds lights on effective construction safety surveillance by integrating human risk recognition capabilities into advanced computer vision.     

Informal learning in work environments: training with the Social Web in the workplace

The Internet and its increasing usage has changed informal learning in depth. This change has affected young and older adults in both the workplace and in higher education. But, in spite of this, formal and non-formal course-based approaches have not taken full advantage of these new informal learning scenarios and technologies. The Web 2.0 is a new way for people to communicate across the Internet. Communication is a means of transformation and knowledge exchange. These are the facts that cannot be obviated by the organisations in their training programmes and knowledge management. This special issue is devoted to investigating how informal learning changes or influences online information in Social Web and training strategies in institutions. In order to do so, five papers will present different approaches of informal learning in the workplace regarding Web 2.0 capabilities.     

Robotics in construction: A critical review of the reinforcement learning and imitation learning paradigms

The reinforcement and imitation learning paradigms have the potential to revolutionise robotics. Many successful developments have been reported in literature; however, these approaches have not been explored widely in robotics for construction. The objective of this paper is to consolidate, structure, and summarise research knowledge at the intersection of robotics, reinforcement learning, and construction. A two-strand approach to literature review was employed. A bottom-up approach to analyse in detail a selected number of relevant publications, and a top-down approach in which a large number of papers were analysed to identify common relevant themes and research trends. This study found that research on robotics for construction has not increased significantly since the 1980s, in terms of number of publications. Also, robotics for construction lacks the development of dedicated systems, which limits their effectiveness. Moreover, unlike manufacturing, construction's unstructured and dynamic characteristics are a major challenge for reinforcement and imitation learning approaches. This paper provides a very useful starting point to understating research on robotics for construction by (i) identifying the strengths and limitations of the reinforcement and imitation learning approaches, and (ii) by contextualising the construction robotics problem; both of which will aid to kick-start research on the subject or boost existing research efforts.     

Modeling the relationship between occupational stressors, psychosocial/physical symptoms and injuries in the construction industry

Injury statistics place the construction industry as a high-risk industry, making it necessary to investigate factors that influence accidents to be able to protect workers. Research was carried out to investigate the relationship existing among occupational stressors, psychological/physical symptoms and accident/injury and work days lost outcomes as experienced by manual workers engaged in a range of industrial construction occupations. Some of the occupational stressors significantly associated with self-reported and OSHA logged injuries were training, job certainty and safety climate of the company. The OSHA logged injuries were associated with the occurrence of headaches and feelings of tenseness on the job. These results imply that non-physical stressors should be included as a potential input associated with injuries in injury risk models for construction workers.

Relevance to industry

Traditional approaches to workers’ safety in the construction industry have focused on the physical and biomechanical aspects of work by improving tools, equipment and task completion methods. The impact of psychosocial factors, specifically stress as experienced by construction workers, is an area of growing research, which is yielding results that suggest overall work safety on the construction site should take into account psychosocial aspects of work.     

Accident patterns and prevention measures for fatal occupational falls in the construction industry

Contributing factors to 621 occupational fatal falls have been identified with respect to the victim's individual factors, the fall site, company size, and cause of fall. Individual factors included age, gender, experience, and the use of personal protective equipment (PPE). Accident scenarios were derived from accident reports. Significant linkages were found between causes for the falls and accident events. Falls from scaffold staging were associated with a lack of complying scaffolds and bodily action. Falls through existing floor openings were associated with unguarded openings, inappropriate protections, or the removal of protections. Falls from building girders or other structural steel were associated with bodily actions and improper use of PPE. Falls from roof edges were associated with bodily actions and being pulled down by a hoist, object or tool. Falls through roof surfaces were associated with lack of complying scaffolds. Falls from ladders were associated with overexertion and unusual control and the use of unsafe ladders and tools. Falls down stairs or steps were associated with unguarded openings. Falls while jumping to a lower floor and falls through existing roof openings were associated with poor work practices. Primary and secondary prevention measures can be used to prevent falls or to mitigate the consequences of falls and are suggested for each type of accident. Primary prevention measures would include fixed barriers, such as handrails, guardrails, surface opening protections (hole coverings), crawling boards/planks, and strong roofing materials. Secondary protection measures would include travel restraint systems (safety belt), fall arrest systems (safety harness), and fall containment systems (safety nets).     

Identification of accident-injury type and bodypart factors from construction accident reports: A graph-based deep learning framework

Accident reports provide information to understand why and how events occur. Learning from past accident reports is critical for preventing accidents or injuries in construction safety management. However, there are two issues: (1) manual analysis of such accident reports is time-consuming and labor-intensive; and (2) previous research mainly focused on analyzing the causal factors of accidents. Not much research concentrates on the injury effect in an accident and the influential relationship between accident cause and injury effect. To tackle this problem, a graph-based deep learning framework is proposed to identify accident-injury type and bodypart factors automatically to enable managers to make timely and better-informed decisions to prevent accidents and injuries for on-site safety. In this framework, a graph-based deep learning approach (specifically, the Graph Convolutional Network) is developed to automatically classify accident reports labeled with accident_type and injury_type, whereas the traversal method is developed to identify the bodypart factors. To further intuitively visualize these safety risk factors (e.g., accident_type, injury_type, and bodypart factors), the co-occurrence networks are drawn to further intuitively reveal the interdependency in accident-injury and injury-bodypart types respectively. From the perspective of theoretical and practical contributions, the framework proposed in this study not only represents a substantial data-driven advancement in construction accident report classification and keyword extraction tasks, but also enables managers to get knowledge of construction safety performance (i.e., accident causes and injury effects) and further formulate corresponding strategies to prevent accidents and injuries in on-site safety management.     

Automatic classification of construction safety reports using semi-supervised YAKE-Guided LDA approach

Literature on supervised Machine-Learning (ML) approaches for classifying text-based safety reports for the construction sector has been growing. Recent studies have emphasized the need to build ML approaches that balance high classification accuracy and performance on management criteria, such as resource intensiveness. However, despite being highly accurate, the extensively focused, supervised ML approaches may not perform well on management criteria as many factors contribute to their resource intensiveness. Alternatively, the potential for semi-supervised ML approaches to achieve balanced performance has rarely been explored in the construction safety literature. The current study contributes to the scarce knowledge on semi-supervised ML approaches by demonstrating the applicability of a state-of-the-art semi-supervised learning approach, i.e., Yet, Another Keyword Extractor (YAKE) integrated with Guided Latent Dirichlet Allocation (GLDA) for construction safety report classification. Construction-safety-specific knowledge is extracted as keywords through YAKE, relying on accessible literature with minimal manual intervention. Keywords from YAKE are then seeded in the GLDA model for the automatic classification of safety reports without requiring a large quantity of prelabeled datasets. The YAKE-GLDA classification performance (F1 score of 0.66) is superior to existing unsupervised methods for the benchmark data containing injury narratives from Occupational Health and Safety Administration (OSHA). The YAKE-GLDA approach is also applied to near-miss safety reports from a construction site. The study demonstrates a high degree of generality of the YAKE-GLDA approach through a moderately high F1 score of 0.86 for a few categories in the near-miss data. The current research demonstrates that, unlike the existing supervised approaches, the semi-supervised YAKE-GLDA approach can achieve a novel possibility of consistently achieving reasonably good classification performance across various construction-specific safety datasets yet being resource-efficient. Results from an objective comparative and sensitivity analysis contribute to much-required knowledge-contesting insights into the functioning and applicability of the YAKE-GLDA. The results from the current study will help construction organizations implement and optimize an efficient ML-based knowledge-mining strategy for domains beyond safety and across sites where the availability of a pre-labeled dataset is a significant limitation.     

Critically classifying: UK e-government website benchmarking and the recasting of the citizen as customer

In recent years, discussion of the provision of government services has paid particular attention to notions of customer choice and improved service delivery. However, there appears to be marked shift in the relationship between the citizen and the state moving from government being responsive to the needs of citizens to viewing citizens explicitly as customers. This paper argues that this change is being accelerated by government use of techniques like benchmarking, which have been widely used in the private sector. To illustrate this point, the paper focuses on the adoption of website benchmarking techniques by the public sector. The paper argues that the essence of these benchmarking technologies, a process comprised of both finding and producing truth, is fundamentally based on the act of classifying and draws on Martin Heidegger's etymological enquiry to reinterpret classification as a dynamic movement towards order that both creates and obfuscates truth. In so doing, it demonstrates how Heidegger's seminal ideas can be adapted for critical social research by showing that technology is more than an instrument as it has epistemic implications for what counts as truth. This stance is used as the basis for understanding empirical work reporting on a UK government website benchmarking project. Our analysis identifies the means involved in producing the classifications inherent in such benchmarking projects and relates these to the more general move that is recasting the relationship between the citizen and the state, and increasingly blurring the boundaries between the state and the private sector. Recent developments in other attempts by the UK government to use private-sector technologies and approaches indicate ways in which this move might be challenged.     

The Chebyshev wavelets operational matrix of integration and product operation matrix

Operational matrices of integration and product based on Chebyshev wavelets are presented. A general procedure for forming these matrices is given. These matrices play an important role in modelling of problems. Numerical examples are given to demonstrate applicability of these matrices.     

The effect of working height on the loading of the muscular and skeletal systems in the kitchens of workplace canteens

Arising from the confirmed high incidence of illness and other signs and symptoms, estimations were made of the loading on the muscular and skeletal systems of kitchen workers. The study included a health questionnaire and the ergonomics examination of 11 kitchens. Problems in the neck-shoulders region appeared particulary frequently in short workers. The symptoms were confirmed to be associated with the raised position of the upper limbs caused by working surfaces which were too high. The worktable with the cutting board was too high for a third of the workers, estimated from individual elbow height. Also, 34–80% of the kitchen equipment was too high. Raising loads above shoulder level into ovens or pressure cookers, for example, loaded the shoulder joints. The back was loaded especially in lifting to knee height and in continuous standing (78% of working time). Loading can be modified by fitting the kitchen with working tables which are adjustable for height (800–950 mm) and by lowering kitchen equipment as follows: cooker height 650 mm, cooking vessel rim height from the floor < 900 mm, oven and pressure cooker rail heights 500–1400 mm.     

Understanding workplace violence: The value of a systems perspective

Workplace violence is a leading form of occupational injury and fatality, but has received little attention from the ergonomics research community. The paper reports findings from the 2012 New Zealand Workplace Violence Survey, and examines the workplace violence experience of 86 New Zealand organisations and the perceptions of occupational health and safety professionals from a systems perspective. Over 50% of respondents reported violence cases in their organisation, with perpetrators evenly split between co-workers and external sources such as patients. Highest reported levels of violence were observed for agriculture, forestry and construction sectors. Highest risk factor ratings were reported for interpersonal and organisational factors, notably interpersonal communication, time pressure and workloads, with lowest ratings for environmental factors. A range of violence prevention measures were reported, although most organisations relied on single control measures, suggesting unmanaged violence risks were common among the sample.     

Prevalence of work-related occupational injuries and its risk factors among brickfield workers in West Bengal,India

The manual brick making process is a physically demanding job with a high risk of work-related injuries. Prevalence of work-related injuries (17.55%) occurs frequently in manual brick making activities due to inherently hazardous nature. This study analyzes 451 recordable incidents that occurred over a period of 7 years (2011–2017) among 220 male and 180 female workers in a different brickfield of West Bengal, India. The leading cause of brickfield injury was fall from heights, struck by objects, overexertion, lack of awareness, slippage of spade while mud collection etc. Carrying bricks and spading are two activities, in which the injuries occurred most among female and male brickfield workers respectively. Risk factors like MMH, prolonged working time, repetitiveness, awkward posture, lack of rotational task, overcrowded work, Lack of personal protective device, and lastly sleep disturbance and poor income are the key factor for work-related injuries. Sprain and strain, cut or laceration, abrasion, avulsion, and snake or insect bite are the main injuries among both groups of brickfield workers. Lower back and toes are the most affected parts of the body followed by ankle, feet, and hand. From this study, it was also observed that female brickfield workers are much more affected than male agricultural workers. The incident rate among male and female brickfield workers was 18.7 per 1000 workers per year and 21.2 per 1000 workers per year, respectively. So due to injuries in both groups of brickfield workers, their health, productivity and work performance were consequently affected.     

Computer vision for behaviour-based safety in construction: A review and future directions

The process of identifying and bringing to the fore people’s unsafe behaviour is a core function of implementing a behaviour-based safety (BBS) program in construction. This can be a labour-intensive and challenging process but is needed to enable people to reflect and learn about how their unsafe actions can jeopardise not only their safety but that of their co-workers. With advances being made in computer vision, the capability exists to automatically capture and identify unsafe behaviour and hazards in real-time from two-dimensional (2D) digital images/videos. The corollary developments in computer vision have stimulated a wealth of research in construction to examine its potential application to practice. Hindering the application of computer vision in construction has been its inability to accurately, and generalise the detection of objects. To address this shortcoming, developments in deep learning have provided computer vision with the ability to improve the accuracy, reliability and ability to generalise object detection and therefore its usage in construction. In this paper we review the developments of computer vision studies that have been used to identify unsafe behaviour from 2D images that arises on construction sites. Then, in light of advances made with deep learning, we examine and discuss its integration with computer vision to support BBS. We also suggest that future computer-vision research should aim to support BBS by being able to: (1) observe and record unsafe behaviour; (2) understand why people act unsafe behaviour; (3) learn from unsafe behaviour; and (4) predict unsafe behaviour.     

Effects of variable lighting intensities and colour temperatures on sulphatoxymelatonin and subjective mood in an experimental office workplace

Workplace illumination is of paramount importance in determining the employee's productivity and well-being. Moreover, light exerts non-visual effects with respect to biological rhythms. In this study, we investigated the impact of different lighting conditions (500-1800 lx, 6500 K; 500 lx, 4000 K) on sulphatoxymelatonin (aMT6-s) and subjective mood in an experimental office accommodation. Urinary aMT6-s concentrations were significantly decreased at all days of the experiment in both lights. On day 3, differences between aMT6-s concentrations in specimen collected at 05:00 p.m. and at 09:00 a.m. were significantly higher under variable lighting conditions. Analyses of a mood rating inventory revealed a benefit of variable light with respect to the dimensions of "Activity", while "Deactivation" and "Fatigue" were increased in regular light on day 1. "Activity", "Concentration", and "Deactivation" changed in opposite directions when comparing variable with regular illumination on two consecutive days. In conclusion, variable light exerts a potential advantage in indoor office accommodations with respect to subjective mood, although no unequivocal differences in the profile of aMT6-s were found as compared to regular light.     

Dynamic probabilistic analysis of accidents in construction projects by combining precursor data and expert judgments

Construction accident occurrences are essentially rare, stochastic, and dynamic. This study proposes a method for accident prediction that fully captures these natures based on historical data and prior knowledge. The method utilizes the relatively high occurrence frequency of precursor events and the dependency between precursors and accidents. The modeling approach consists of three steps: (1) characterize the stochastic occurrences of precursor events over time based on precursor data; (2) estimate the failure rate of the Poisson model which is assumed to be a prior distribution of accident occurrences; and (3) elicit the expert knowledge about the stochastic dependency between near miss occurrences and accident occurrences. A copula-based Markov model is used to develop the time series model of precursors while a copula-based protocol is proposed to aid expert judgment elicitation and quantification. The probability of accident occurrence is then dynamically updated according to the observed historical near miss numbers. The proposed method is applied to a metro construction project. A five-year long near miss data were collected and used as accident precursor data, while three experts were invited to provide relevant information. The developed accident model is used to predict the accident-prone periods, which are consistent with the months that the observed near miss occurrence frequency deviates significantly from normality. Thus, the model can be used to support the planning of necessary safety improvement programs before the accident risk increased.