The aim of this paper is to enhance readers' understanding of research design strategies, past and present, for studying nursing workloads. Future research directions are also discussed. Nursing workloads are associated with nurse burnout and turnover. During our current global nursing shortage, researchers must identify ways to mitigate nurses' heavy workloads. Relevant, prior nursing workload research is presented with brief descriptions of designs, methods and findings. To illustrate the current complexity of nurses' work environments and the myriad factors that influence nurses' workloads, this paper features the ongoing nursing workload research of two Canadian research teams with different methodological approaches. These two teams are employing current research innovations, such as human factors multi-systems frameworks, design thinking, simulation modeling and integrated knowledge translation. With respect to future research implications, the teams are melding methods and tools to promote a more sophisticated way of understanding the complex linkages between patient needs, systems design and the management of nurses' workloads.

This study uses Digital Human Modelling (DHM) and Discrete Event Simulation (DES) to examine how caring for COVID-19-positive (C+) patients affects nurses' workload and care-quality. DHM inputs include: nurse anthropometrics, task postures, and hand forces. DES inputs include: unit-layout, patient care data, COVID-19 status & impact on tasks, and task execution-logic. The study shows that reducing nurses' biomechanical workload increases mental workload and decreases direct patient care, potentially leading to stress, burnout, and errors. Compared to pre-pandemic conditions, when nurses were assigned five C+ patients, cumulative bilateral shoulder moments and lumbar load decreased by 38%, 36%, and 46%, respectively. However, this was accompanied by increases in mental workload (242%), task waiting-time (70%), and missed-care (353%). These effects were driven by the large increase in required infection control routines. Combining DHM and DES can help evaluate workplace/task designs and provide valuable insights for healthcare system design-policy setting and operational management decision-making.

Human robot collaboration is becoming the norm in the workplace, due to the benefits robots can bring to efficiency and production. However, this creates highly complex and dynamic workplaces that human operators need to adapt to. Industry 5.0 promotes the use of robotics and smart technologies in a more human-centric way. However, research on how operators are affected by those changes is needed to better understand how to move towards human-centricity. As such, an experimental study was designed and performed on human robot collaborative assembly. The main aim was to investigate the correlation between cognitive load and quality due to collaboration. Here, the preliminary results of the experimental study are presented in order to remark relevant states influencing work allocation. The results showcased the need for better training and more knowledge for the operators, as well as involving operators in process and workplace design. This study helps contribute knowledge on robot implementation and process design for human robot collaboration for both researchers and operations management, as it showcases the need to involve operators in those steps due to the feedback they can provide due to their experience.

This study proposes a generic approach for creating human factors-based assessment tools to enhance operational system quality by reducing errors. The approach was driven by experiences and lessons learned in creating the warehouse error prevention (WEP) tool and other system engineering tools. The generic approach consists of 1) identifying tool objectives, 2) identifying system failure modes, 3) specifying design-related quality risk factors for each failure mode, 4) designing the tool, 5) conducting user evaluations, and 6) validating the tool. The WEP tool exemplifies this approach and identifies human factors related to design flaws associated with quality risk factors in warehouse operations. The WEP tool can be used at the initial stage of design or later for process improvement and training. While this process can be adapted for various contexts, further study is necessary to support the teams in creating tools to identify design-related human factors contributing to quality issues.

This research examines the status of human factors and ergonomics (HF/E) metrics in the case context of product realisation in an electronics manufacturing company. Interactions with 100+ stakeholders over a five year period were thematically analysed for metrics-related views and content. A disconnect between engineering metrics and HF/E metrics was evident. Engineers and HF/E specialists expressed different understandings of the gap between the disciplines and how to generate HF/E metrics that would fit the organisation. Other emerging themes provided insight for metrics development including improving indicator relatability, considerations for communication of information, and barriers to implementation of metrics. The results led to seven recommendations to help guide practitioners in developing and refining HF/E metrics as part of an organisation's metrics system. This macroergonomic case study provides key points for consideration when developing HF/E focussed metrics to support organisations being more proactive with HF/E in work system design. Practitioner summary: Metrics' presence, stakeholder views on metrics, and metrics-related content in a case organisation were thematically analysed with a macroergonomics focus. Human factors and ergonomics metrics (HF/E) were disconnected from engineering metrics thus limiting the design team's ability to handle human factors in design. Factors influencing HF/E metrics creation and integration were identified, resulting in seven recommendations for developing HF/E metrics.

This paper presents an evaluation of the usability, functionality, and usefulness of the Warehouse Error Prevention (WEP) tool that consists of seven modules. The WEP tool is framed in a simple yes/no form, which can be used to identify human factors related to sources of pick errors in a warehouse. Thirty-three participants in 27 organisations from three different countries participated in a trial application and evaluation of the tool. The evaluation included a survey study and semi-structured interviews. Survey results show that participants agreed on the usability and functionality of the WEP tool. In the interviews, participants generally reported the WEP tool as being both accurate and functional with the potential to support engineers, ergonomists, and warehouse managers to improve order picking quality. Further quantitative field testing of the WEP tool's potential to identify costly warehouse errors is needed.

As one of many initiatives underway in a collaborative action research project with a large manufacturer, this paper presents the development of a "human factors" failure modes effect analysis (HF-FMEA). FMEA is an engineering reliability tool that helps define, identify, prioritize and eliminate known or potential failures of a system, design or manufacturing assembly process, generally to optimize quality or systems safety for consumers. The goal of the HF-FMEA is to detect and minimize risk of injury for the operator who will assemble products, prior to design of an assembly line. Scoring procedures for "severity", "occurrence" and "detection" from a HF perspective are presented with examples. Embedding the HF-FMEA into software templates, and structuring a process for support and integration helps ensure its continued use. The process may be useful for other organizations with hand-intensive assemblies to optimize worker health together with assembly quality.

This paper describes one initiative in a 3 year-collaboration between Research In Motion (RIM) and Ryerson University, the goal of which is to integrate human factors (HF) considerations into the process of designing assembly systems. The RIM-Ryerson steering group suggested this initiative because the engineering group was formalizing their fixture development process with the goal of improving the quality and timeline for fixture design. To incorporate HF into design, research has suggested that the combination of a few specific HF design criteria and active involvement of HF specialists are both critical for positive outcomes. In this initiative, Ergonomists analyzed current assembly fixtures for ergonomics-related concerns. These were shared with nine design engineers in a workshop with a goal of translating the concerns into design guidelines that would prevent the concern. The workshop resulted in 12 design guidelines that are now ergonomic requirements for internal or external vendors. The new fixture development process now includes four process stages where the Ergonomist, working proactively as a design team member, ensures the design meets ergonomics requirements. The stages are: fixture design kick-off meeting to clarify design requirements and initiate the DFMEA (design failure modes effects analysis); the fixture design review; the production tool design sign-off; and lessons learned. The combination of ergonomic design-for-fixture guidelines and the participation of Ergonomists in the fixture design process have the potential for improving assembly ergonomics and quality across thousands of workers.

This dissertation takes an exploratory look at the role of human factors (HF) metrics within an electronics manufacturing organization by focussing on three objectives: 1) determining company stakeholder views of HF metrics, metrics development and HF application, 2) developing a workstation level HF assessment tool for light assembly work, and 3) creating a tool that reports the level of HF integration and maturity in an organization. Mixed methods were used in an action research framework. Research at the case organization was predominantly qualitative and included field notes, audio recordings, and company documents. Identified gaps between engineering and HF metrics were due to HF metrics focussed more on health and safety measures and activities being completed, gaps in the understanding of HF contributions, and the need for new HF tools to generate reporting measures. Five identified themes affecting HF metrics development included 1) knowledge of engineer processes and of HF principles, 2) connection of metrics to the organization, 3) support of the organization and ofthe information to the organization, 4) resource availability and limitations, and 5) communication format of metrics information. Collaborative user-centered development of a workstation efficiency evaluator tool helped determine data of interest and effective communication of output variables for users. Design stage inputs create outputs that include HF and system information. The tool performed well in a comparison to an observation-based analysis and also demonstrated tolerance to input errors on workstation outcomes. The developed Human Factors Integration Tool assesses HF maturity across organizational functions. Face and content validity of the tool were tested in field testing and workshops. Participants communicated a need for the tool and its contents. Industry stakeholders found the consensus-based tool helped to establish the status of HF in the organization, plan projects to further develop HF capabilities, and initiate discussions on HF for performance and well-being. The created tools demonstrated approaches to the development of future HF tools. These dissertation findings illustrate the need for more HF metric work, including developing HF measures that contribute to organization metrics, and that the development of HF measures and processes need HF considerations in their development.

Addressing the need for a virtual tool to predict operator load in light assembly work, a method is presented to estimate shoulder load and hand movement from layout parameters. Using three-dimensional representation of a task location relative to the seated workstation, a regression model is used to predict operator shoulder load. Hand locations for each task of the work cycle are used to determine cumulative hand movement and shoulder load. A case application of the virtual tool showed that trends from an observational tool used by Neumann et al (2002) in a workstation comparison were matched. The virtual tool predicted shoulder load values 19.8-32.8% higher than the observational tool however this was attributed to its use of three-dimensional task analysis and a different shoulder model. Future work for the virtual tool will assess reliability, validity, the effect of underlying tool assumptions, and the incorporation of a movement time prediction method.

Nursing is a high musculoskeletal disorder (MSD) risk job with high workload demands. This study combines Digital Human Modelling (DHM) and Discrete Event Simulation (DES) to address the need for tools to better manage MSD risk. This novel approach quantifies physical-workload, work-performance, and quality-of-care, in response to varying geographical patient-bed assignments, patient-acuity levels, and nurse-patient ratios. Lumbar loads for 86 care-delivery tasks in an acute care hospital unit were used as inputs in a DES model of the care-delivery process, creating a shift-long time trace of the biomechanical load. Peak L4/L5 compression and moment were 3574 N and 111.58 Nm, respectively. This study reports trade-offs in all three experiments: (i) increasing geographical patient-bed assignment distance decreased L4/L5 compression (8.8%); (ii) increased patient-acuity decreased L4/L5 moment (4%); (iii) Increased nurse-patient ratio decreased L4/L5 compression (10%) and moment (17%). However, in all experiments, Quality of care indicators deteriorated (20, 19, and 29%, respectively). Practitioner Summary: This research has the potential to support decision-makers by developing a simulation tool that quantifies the impact of varying operational and design-policies in terms of biomechanical-load and quality of care. The demonstrator-model reports: as geographical patient-bed distance, patient-acuity levels, and nurse-patient ratios increase, biomechanical-load reduces, and quality of care deteriorates.

Warehouse Error Prevention Checklist Tool: Manual Order Picking (OP) is a labor-intensive and time-consuming process. Poor human and OP system interactions can cause pick errors. The Warehouse Error Prevention Checklist tool (WEP) is to be used by warehouse designers and engineers for an assessment of the interaction of humans with the order picking system design to minimize the pick error occurrence. To do so, Human Factors (HF) which is a multidisciplinary scientific discipline which aims to optimize system performance and an individual’s well-being by understanding the system design elements and human-system interaction (IEA-Council 2000) has been considered. This tool consists of specific design criteria which are classified based on the OP design elements (Module 1- 5) and organizational behavior and facility design management (Module 6) with respect to HF as follows: · Module 1: shelf layout/layout design: This module includes defined facility and shelf layout to minimize an order picker’s physical and cognitive loads. · Module 2: pick information and technology design: This module includes defined criteria needed to avoid exceeding order picker’s perceptual demands by minimizing reading mistakes while using traditional picklist and reading labels. The new pick technology criteria have been defined to reduce order pickers’ cognitive, perceptual, and physical demands. · Module 3: storage assignment design: This module includes defined criteria on item allocation to the storage location to minimize an order picker’s physical and cognitive loads. · Module 4: palletizing/batching: This module includes defined item batching and stacking criteria to minimize an order picker’s physical and cognitive loads. · Module 5: routing: This module includes defined routing strategy to minimize an order picker’s physical and cognitive loads. · Module 6: organizational behaviour management: This module includes defined psychosocial factors such as motivation and training to minimize an order picker’s psychosocial demands. Also, it contains defined environmental criteria such as light, noise, and temperature to minimize an order picker’s perceptual demands

Many methods track company performance and process integration for quality, productivity, environment and safety. Similar methods do not exist for human factors (HF) even though it has impact on these outcomes. Without a HF specific assessment method it is impossible for managers to know if they are achieving 'world class' HF integration. An assessment tool is under development to address this need. The tool assesses the capability of each functional unit in an organization to manage HF aspects in their processes. This includes organizational strategy, design, maintenance, operations, logistics, marketing, and human resources, among others. For each department, the presence of HF aspects including indicators, process flows, and methods are evaluated. The maturity level of HF integration for each is rated in five classifications, conceptually similar to the Baldridge criteria, to reflect its level of 'world class'. The tool is non-prescriptive as it recognises the validity of different integration approaches. Progression to world class means HF works proactively becoming part of the organization's culture. With this tool companies can evaluate their ability to benefit from HF integration on an ongoing basis. It also provides a quantitative method for research and to benchmark macroergonomic capability in other organizations.

Higher acuity levels in COVID-19 patients and increased infection prevention and control routines have increased the work demands on nurses. To understand and quantify these changes, discrete event simulation (DES) was used to quantify the effects of varying the number of COVID-19 patient assignments on nurse workload and quality of care. Model testing was based on the usual nurse-patient ratio of 1:5 while varying the number of COVID-19 positive patients from 0 to 5. The model was validated by comparing outcomes to a step counter field study test with eight nurses. The DES model showed that nurse workload increased, and the quality of care deteriorated as nurses were assigned more COVID-19 positive patients. With five COVID-19 positive patients, the most demanding condition, the simulant-nurse donned and doffed personal protective equipment (PPE) 106 times a shift, totaling 6.1 hours. Direct care time was reduced to 3.4 hours (-64% change from baseline pre-pandemic case). In addition, nurses walked 10.5km (+46% increase from base pre-pandemic conditions) per shift while 75 care tasks (+242%), on average, were in the task queue. This contributed to 143 missed care tasks (+353% increase from base pre-pandemic conditions), equivalent to 9.6 hours (+311%) of missed care time and care task waiting time increased to 1.2 hours (+70%), in comparison to baseline (pre-pandemic) conditions. This process simulation approach may be used as potential decision support tools in the design and management of hospitals in-patient care settings, including pandemic planning scenarios.

The work environment (WE) reporting content in corporate social responsibility (CSR) reporting is inconsistent and is rarely a complete representation of organizations' WEs. This study examines existing WE reporting guidance with respect to the definition and dimensions of WE in CSR literature and WE-related standards. As a result, a working definition for WE is proposed as referring to "all aspects of the design and management of the work system that affect the employee's interactions with the workplace". A review of the WE components in the CSR literature and of the components in WE analysis instruments resulted in the development of a worker-centric set of 12 WE dimensions. The WE dimensions identified included: 1) Job demands; 2) Health and wellbeing management and outcomes; 3) Work environment design and maintenance; 4) Learning and development; 5) Work control; 6) Leadership structure, support, and worker participation; 7) Work structure and stability; 8) Work-life balance and work experience/performance; 9) Respect and inclusion; 10) Recognition and benefits; 11) Work type and location; and 12) External factors of influence. These proposed dimensions were used as a basis to compare the inclusion and quality of WE reporting guidance from 14 relevant WE standards on a 0–4 scale. The quality of reporting guidance in all standards was low, with the GRI and ISO 30414 providing guidance for the highest quality of reporting. Only the CAN/CSA-ISO 26000 guidelines included any advice on all dimensions. These results indicate a systematic weakness with available WE reporting standards and a need to develop more comprehensive reporting guidance for companies that should also include consideration of contractors, suppliers, and supply chain WEs.

This paper reports on the Patient Safety Research and Application Competition that was held in conjunction with the 2021 International Ergonomics Association Conference. The objectives of this competition were to: (1) Formulate research problem statements and innovative solutions to improve patient safety through the application of human factors/ergonomics (HF/E) to the healthcare system, (2) Showcase how the HF/E approach to this topic can lead to a useful, usable, and satisfying user experience while simultaneously improving outcomes relating to both functional and non-functional requirements, and (3) Provide an effective way of engaging students and early career researchers in IEA activities and initiatives. After reviewing the patient safety topics that motivated the design competition, we then report on the work carried out by the two finalists in the competition, and discuss lessons learned. We propose the continued use of design competitions in the future to motivate and showcase ergonomic problem-solving design by students and early career researchers and practitioners.

This paper reports on the Patient Safety Research and Application Competition that was held in conjunction with the 2021 International Ergonomics Association Conference. The objectives of this competition were to: (1) Formulate research problem statements and innovative solutions to improve patient safety through the application of human factors/ergonomics (HF/E) to the healthcare system, (2) Showcase how the HF/E approach to this topic can lead to a useful, usable, and satisfying user experience while simultaneously improving outcomes relating to both functional and nonfunctional requirements, and (3) Provide an effective way of engaging students and early career researchers in IEA activities and initiatives. After reviewing the patient safety topics that motivated the design competition, we then report on the work carried out by the two finalists in the competition, and discuss lessons learned. We propose the continued use of design competitions in the future to motivate and showcase ergonomic problem-solving design by students and early career researchers and practitioners.

COVID-19 is taking a significant toll on front-line healthcare professionals - especially nurses who provide care for patients 24/7. Given the trend for higher acuity levels among the COVID-19 patients and increased infection prevention and control (IPAC) precautions, such as donning and doffing personal protective equipment (PPE), the demands on front-line healthcare professionals have changed. To understand the changes, discrete event simulation (DES) was used to quantify the effects of varying COVID-19 policies on nurse workload and quality of care. We are testing a standard nurse-patient ratio of 1:5 where we vary the number of COVID-19 positive patients in that mix from 1 to 5. Preliminary modeling results show as nurses were assigned to more COVID-19 positive patients, the workload of nurses increased, and quality of care deteriorated. In comparison to the baseline (pre-pandemic) case, distance walked by simulant-nurse, mental workload, direct care time, missed care, missed care delivery time and care task waiting time, increased by up to 40%, 279%, −27%, 132%, 311% and 44%, respectively. The developed approach has implications for design of the healthcare system as a whole, including pandemic planning scenarios.

This paper presents the development of a tool that allows an organization to assess its level of human factors (HF) and ergonomics integration and maturity within the organization. The Human Factors Integration Toolset (available at: TBD) has been developed and validated through a series of workshops with 45 participants from industry and academia and through industry partnered field-testing. HF maturity is assessed across five levels in 16 organizational functions based on any of 31 discrete elements contributing to HF. Summing element scores in a function determines a percent of ideal HF for the function. Industry stakeholders engaged in field-testing found the tool helped to establish the status of HF in the organization, plan projects to further develop HF capabilities, and initiate discussions on HF for performance and well-being. Improvement suggestions included adding an IT function, refining the language for non-HF specialists, including knowledge work, and creating a digital version to improve usability. Practitioner Summary A tool scoring HF capability in 16 organization functions has been developed collaboratively. Industry stakeholders expressed a need for the tool and provided validation of tool design decisions. Fieldtesting improved tool usability and showed that, beyond scoring HF capability, the tool created opportunities for discussions of HF-related improvement possibilities. Keywords: Macroergonomics, ergonomics strategy, organizational design and management, process management, operations management This paper was awarded a Liberty Mutual Award for 2020.

This paper presents the development and proof of concept of a tool to predict worker and system performance using inputs of work element descriptions and hand locations from a seated, light assembly workstation layout. Tool inputs can be obtained in the design stage. Tool outputs include human factors (shoulder load, hand movement, reach zone acceptability) and system (element time and cycle time) information. Shoulder loads are predicted from two dimensional shoulder models created from a digital human model. The tool is demonstrated on a previous observation based assessment of a workstation redesign. Results reflected the findings of the observation assessment, but also provided more work cycle information as well as cumulative, work shift information. The tool enables prediction of workload and task performance times from design stage parameters without the need of an ergonomist. It can be used to predict critical components of the layout and plan workflow based on worker, workstation, and task information. The tool is available for free download at: www.researchgate.net/project/Workstation-Efficiency-Evaluator-WEE-Tool.