Human And Organizational Factors Research Articles

Exploring human factors of major chemical accidents in China: Evidence from 160 accidents during 2011–2022

Major chemical accidents (MCAs) usually cause multiple casualties, property losses, and environmental damage. MCAs in hazardous chemical production enterprises (HCPEs) in China have been frequent in recent years. Understanding past accidents is essential for accident prevention and risk mitigation. However, no accurate statistical analysis and systematic human cause investigation of MCAs in China is available. Hence, this study systematically identified the main characteristics and human factors of MCAs in China during 2011–2022 using official accident investigation reports. First, the 160 MCAs were investigated by mathematical and statistical methods involving eight main characteristics: evolution trend, spatial locations, accident types, domino effects involved, occurrence links, time volatility, hazardous chemicals involved, and enterprise scales. Second, based on the characteristics of MCAs and existing research on chemical accidents, the Human Factors Analysis and Classification System (HFACS) for Chemical Accidents (HFACS-CA), which consists of 26 cause categories and is suitable for the analysis of chemical accidents, was proposed on the basis of HFACS and other extended HFACS. Then, the selected 146 representative MCAs were analyzed using the HFACS-CA framework; the human factors (comprising human and organizational factors (HOFs) and outside factors) of MCAs in China were systematically explored. Finally, according to the main characteristics and human factors of MCAs, comprehensive recommendations for the prevention of MCAs involving enterprises, governments, and interested parties were proposed. This work can provide a useful human factor analysis tool for investigating chemical accidents and valuable information for preventing MCAs in China and other countries.

A novel framework for human factors analysis and classification system for medical errors (HFACS-MES)-A Delphi study and causality analysis.

The healthcare system (HCS) is one of the most crucial and essential systems for humanity. Currently, supplying the patients' safety and preventing the medical adverse events (MAEs) in HCS is a global issue. Human and organizational factors (HOFs) are the primary causes of MAEs. However, there are limited analytical methods to investigate the role of these factors in medical errors (MEs). The aim of present study was to introduce a new and applicable framework for the causation of MAEs based on the original HFACS. In this descriptive-analytical study, HOFs related to MEs were initially extracted through a comprehensive literature review. Subsequently, a Delphi study was employed to develop a new human factors analysis and classification system for medical errors (HFACS-MEs) framework. To validate this framework in the causation and analysis of MEs, 180 MAEs were analyzed by using HFACS-MEs. The results showed that the new HFACS-MEs model comprised 5 causal levels and 25 causal categories. The most significant changes in HFACS-MEs compared to the original HFACS included adding a fifth causal level, named "extra-organizational issues", adding the causal categories "management of change" (MOC) and "patient safety culture" (PSC) to fourth causal level", adding "patient-related factors (PRF)" and "task elements" to second causal level and finally, appending "situational violations" to first causal level. Causality analyses among categories in the HFACS-MEs framework showed that the new added causal level (extra-organizational issues) have statistically significant relationships with causal factors of lower levels (Φc≤0.41, p-value≤0.038). Other new causal category including MOC, PSC, PRF and situational violations significantly influenced by the causal categories of higher levels and had an statistically significant effect on the lower-level causal categories (Φc>0.2, p-value<0.05). The framework developed in this study serves as a valuable tool in identifying the causes and causal pathways of MAEs, facilitating a comprehensive analysis of the human factors that significantly impact patient safety within HCS.

Human and organizational factors influencing structural safety: A review

A broad review of the existing literature concerning Human and Organizational Factors (HOFs) and human errors influencing structural safety is presented in this study. Publications on this research topic were collected from the Scopus database. Two research focal points of this topic, namely modelling and evaluating the human error effects on structural reliability, and identifying causal factors for structural defects and failures, have been recognized and discussed with an in-depth literature review. The review of studies with a model focus summarizes the models and methods that have been developed to evaluate structural reliability considering human error effects. Besides, the review of publications on the factor subject outlines the most acknowledged HOFs that influence structural safety. Moreover, an additional spotlight was given to the studies from the offshore industry for the advanced development in HOFs and contributing the first complete Human Reliability Analysis (HRA) method for structural reliability analysis. In conclusion, this study provides a holistic overview of the knowledge developed in existing research on the topic of HOFs and human error influencing structural safety. Furthermore, current developments and challenges are reflected, and future research directions are explored for academics entering and working in this field. Additionally, the insights into HOFs generated from this review can assist engineers with better hazard identification and quality assurance in practice.

Human Reliability Assessment method applied to investigate human factors in NDT - The case of the interpretation of radiograms in the French nuclear sector

This communication reports on a study carried out in the context of the collaborative FOEHN project (Human and Organizational Factors in Non-Destructive Evaluation) supported by the French National Research Agency. The motivation of this project comes from the observation that human and Organizational factors (HOF) are not sufficiently considered by the NDT community. Its goal is to analyse and model the influence of the HOF on selected cases of study in the perspective of a better evaluation of the performance of inspections. The communication is focused on a radiographic test (RT) case of study in which it appeared that several successive inspections had failed to detect an existing in-service defect. The analysis and modelling of HOF related to interpretation of films has been achieved in the framework of the CREAM (Cognitive and Reliability and Error Analysis Method). A survey has been conducted during the training and the maintaining of the proficiency of NDT (Non Destructive Testing) operators. This was followed by a non-participant observation of operators on site and several individual interviews including a sample of people covering the main organizational and hierarchical roles (eg. project management, management, operations, invigilation). The exchange with the HOF experts resulted in a hierarchical analysis of "radiogram interpretation" tasks (31 sub-tasks) and a list of contextual and organizational factors that may affect the performance of interpretation of films by the operator. From such a description the CREAM method allows to determine critical tasks and probability of “errors” linked to a limited set of “Common Performance Conditions” (CPC). The first conclusions of this study are that the model CREAM seems well-adapted to the estimation of the impact of HOF on NDT performances. The next phases should be to apply it to other tasks (here only radiograph interpretation) and techniques. The expected benefit of this study is to provide tools for the evaluation and optimisation of NDT implementation.

A data-driven narratives skeleton pattern recognition from accident reports dataset for human-and-organizational-factors analysis

Accidents in the process industry involve several interacting factors, including human and organizational factors (HOFs). A long-standing obstacle to HOFs analysis is lack of data. Accident reports are an essential data source to learn from the past and contain HOFs-related data, but they are usually unstructured text in a not standardized format. Some studies have explored the extraction of information automatically from accident reports based on Natural Language Processing (NLP) techniques. However, they were not dedicated to HOFs. Risk communication is considered an essential pillar in safety and risk science. This research develops a HOFs-focused risk communication framework based on the NLP techniques that can support risk assessment and mitigation. The proposed approach automatically extracts the target groups oriented “Who, When, Where, Why” (4Ws) information from accident reports.This framework was applied to explore the eMARS database. The results show that the “4Ws” skeleton of narratives has appreciated performance in pattern recognition and holistic information analysis. The graphical representation interfaces are designed to display the features of HOFs-related accidents, which can better be communicated to the sharp-end operators and decision-makers.

Quantifying and comparing the effects of human and organizational factors in electric maloperation accidents with HFACS–CatBoost and SHAP

AbstractThe proportion of electric maloperation accidents (EMAs) in substations caused by human and organizational factors (HOFs) has gradually increased. Although there has been some research into the factors affecting EMAs in substations, the available results are insufficient to support the interpretation of HOFs in EMAs. This article explores the relationships between the HOFs and EMAs using Human Factors Analysis and Classification System‐gradient boosting with categorical features support (HFACS–CatBoost) and Shapley Additive exPlanation (SHAP) methods. First, the HFACS framework was introduced to identify 135 EMAs in the Southern Power Grid risk causation. CatBoost was used to construct an accident classification model to analyze the important relationship between accidents and HOFs and to compare and analyze with the extreme gradient boosting (XGBoost) and the binary logistic regression (BLR) to verify the superiority of CatBoost. Finally, to solve the problem of inadequate interpretation of the CatBoost black‐box model, the SHAP value plot was applied to express the contribution degree relationship between accidents and HOFs. The results show that the above method can explore and explain the importance and contribution of HOFs in EMAs. And from this, it is concluded that poor psychological state, poor communication and coordination, inadequate supervision, and inadequate training and education are highly correlated with the occurrence of EMAs. The findings will help substation operations and maintenance staff to develop safety measures to address the confusion of HOFs in substations and prevent the occurrence of EMAs.

Assessment of COVID-19 barrier effectiveness using process safety techniques

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a respiratory illness called the novel coronavirus 2019 (COVID-19). COVID-19 was declared a pandemic on March 11, 2020. Bow tie analysis (BTA) was applied to analyze the hazard of SARS-CoV-2 for three receptor groups: patient or family member at the IWK Health Centre in acute care, staff member at a British Columbia Forest Safety Council (BCFSC) wood pellet facility, and staff member at the Suncor refinery in Sarnia, Ontario. An inherently safer design (ISD) protocol for BTA was used as a guide for evaluating COVID-19 barriers, and additional COVID-19 controls were recommended. Two communication tools were developed from the IWK bow tie diagram to disseminate the research findings. This research provides lessons learned about the barriers implemented to protect people from contracting COVID-19, and about the use of bow tie diagrams as communication tools. This research has also developed additional example-based guidance that can be used for the COVID-19 pandemic or future respiratory illness pandemics. Recommended future work is the application of BTA to additional industries, the consideration of ISD principles in other control types in the hierarchy of controls (HOC), and further consideration of human and organizational factors (HOF) in BTA.

A new approach for quantitative risk assessment of gas explosions on FPSO

Gas explosions on floating, production, storage and off-loading units (FPSO) cause catastrophic consequences. It is vital to perform an accurate risk assessment for preventing these hazards. This paper presents a comprehensive quantitative risk assessment approach for evaluating the whole gas explosion process from initial leakage to final explosion. A stochastic sampling method is proposed to determine the explosion scenarios by using historical statistics data. Secondly three operational barriers: gas detection, emergency shutdown and ignition prevention are arranged to prevent the development from leakage to explosion. The sequential Bayesian Network (BN) predictive model is constructed to describe the hazard development by considering Human and organizational factors (HOFs). As low as reasonably practicable (ALARP) principle is performed to assess the explosion risks. The proposed approach has been applied to a case study focusing on estimating the risk levels for humans and constructions. It turns out that HOFs have significant effect on the normal functions for operational barriers, if ignoring the HOFs effect the accident risks will be underestimated. This approach is especially good at updating the results whenever new data becomes available. Several safety measures can be recommended based on the diagnostic analysis of BN.

Human Factors in the Model of Urban Fire Spread in Madrid (Spain) Focused on the Poor Population

This study aims to highlight the great potential advantages of bringing human and organizational factors (HOF) into the planning for building fire safety in deprived neighbourhoods (whose populations suffer from a lack of safety culture). Physics-based models were used to analyse fire-spread behaviour in a block of the district of Tetuán, located in the centre of Madrid (Spain), in which a high number of substandard dwellings presented a greater fire risk. GIS tools were used to model the real geometry of the buildings. The numerical models introduced more realistic fire load data related to the characteristics of the population living in these dwellings, which is also a parameter that directly affects the probability of ignition, defined as a Poisson distribution. Generally, the results show that vertical fire spread becomes faster for all buildings, which also contributes to increasing the number of affected rooms. The introduction of HOF in these numerical models can help citizens to better understand fire risk in their own dwellings, raising their risk awareness and subsequently improving their resilience to possible fire accidents.

Interactions among the human and organizational factors within the public sector regarding epidemic prevention and control.

The management of human and organizational factors (HOFs) within the public sector directly concerns the efficacy of epidemic prevention and control (EPC). Insufficient examination of such HOFs has led to defective countermeasures. This study attempts to comprehensively identify the HOFs within the public sector critical to EPC and investigate their interactions with the weighted network theory. A total of 55 HOFs were identified, and their interactions were assessed and visualized in the Chinese context. Then, the established weighted network was analyzed to investigate the interactions and diagnose critical factors and sectors. The analysis shows that there are strong interactions among HOFs, and that the human and organizational risks emerging from administrative departments of public health, centers for disease control and prevention, and medical institutions act as the key risk sources in the complex interconnected EPC system, exacerbating risk and causing a significant domino effect. It is recommended that the authorities devote more resources to the core sectors and endeavor to reinforce those critical HOFs by implementing closer risk communication, collaboration, and response. This study may deepen and broaden the authorities' awareness and understanding of interactions among HOFs regarding epidemic mitigation, and strengthen their capacity to perceive, evaluate, and manage these factors in a proactive and effective way, thereby facilitating the success of EPC.

Systematically investigating human and organisational factors in complex socio-technical systems by using the “SAfety FRactal ANalysis” method

In order to manage the performance of socio-technical systems in a safe and sustainable way, the importance of looking at human and organisational factors (HOF) and their contribution to adverse events is widely recognised. In reality, however, the scope of accident and incident investigations stays usually limited to investigating the immediate causes and decision-making processes related to the accident sequence (e.g. Antonsen, 2009). Important factors, including design and planning decisions, contributing to accidents are hereby often overlooked and the weaknesses in the Safety Management System are hardly ever analysed. The SAfety FRactal ANalysis (SAFRAN) method (Accou and Reniers, 2019) can guide investigators in an intuitive and logic way, to ask questions that help to gain deeper understanding of the capability of organisations to monitor and manage safety critical variability. The essence of using the SAFRAN method for evaluating the performance of the different processes in a socio-technical system, is to approach them in a similar way, building on the generic elements that compose a SMS and systematically looking at the HOF that influenced actions and decision making, regardless of the hierarchical level they are situated at. This paper presents the SAFRAN method, specifying its HOF taxonomy and sharing examples of supporting HOF questions. The approach enables non-experts in HOF to systematically identify the different elements that introduce critical variability in performance and to recognise what additional expertise can be called upon when needed.

Applications of FFTA–HFACS for Analyzing Human and Organization Factors in Electric Misoperation Accidents

Human and organizational factors (HOFs) play an important role in electric misoperation accidents (EMAs), but research into the reliability of human factors is still in its infancy in the field of EMAs, and further investment in research is urgently required. To analyze the HOFs in EMAs, a hybrid method including the Human Factors Analysis and Classification System (HFACS) and fuzzy fault tree analysis (FFTA) was applied to EMAs for the first time in the paper. HFACS is used to identify and classify the HOFs with 135 accidents, reorganized as basic events (BEs), intermediate events (IEs), and top event (TE), and develop the architecture of fault tree (FT). Fuzzy aggregation is employed to address experts’ expressions and obtain the failure probabilities of the BEs and the minimal cut sets (MCSs) of the FT. The approach generates BEs failure probabilities without reliance on quantitative historical failure statistics of EMAs via qualitative records processing. The FFTA–HFACS model is applied for quantitative analysis of the probability of failure of electrical mishaps and the interaction between accident risk factors. It can assist professionals in deciding whether and where to take preventive or corrective actions and assist in knowledgeable decision-making around the electric operation and maintenance process. Finally, applying this hybrid method to EMAs, the results show that the probability of an EMAs is 1.0410 × 10−2, which is a risk level that is likely to occur and must be controlled. Two of the most important risk factors are habitual violations and supervisory violation; a combination of risk factors of inadequate work preparation and paralysis, and irresponsibility on the part of employees are also frequent errors.

Correlation in Causality: A Progressive Study of Hierarchical Relations within Human and Organizational Factors in Coal Mine Accidents.

It has been revealed in numerous investigation reports that human and organizational factors (HOFs) are the fundamental causes of coal mine accidents. However, with various kinds of accident-causing factors in coal mines, the lack of systematic analysis of causality within specific HOFs could lead to defective accident precautions. Therefore, this study centered on the data-driven concept and selected 883 coal mine accident reports from 2011 to 2020 as the original data to discover the influencing paths of specific HOFs. First, 55 manifestations with the characteristics of the coal mine accidents were extracted by text segmentation. Second, according to their own attributes, all manifestations were mapped into the Human Factors Analysis and Classification System (HFACS), forming a modified HFACS-CM framework in China’s coal-mining industry with 5 categories, 19 subcategories and 42 unsafe factors. Finally, the Apriori association algorithm was applied to discover the causal association rules among external influences, organizational influences, unsafe supervision, preconditions for unsafe acts and direct unsafe acts layer by layer, exposing four clear accident-causing “trajectories” in HAFCS-CM. This study contributes to the establishment of a systematic causation model for analyzing the causes of coal mine accidents and helps form corresponding risk prevention measures directly and objectively.

Application of the HFACS-PV approach for identification of human and organizational factors (HOFs) influencing marine accidents

Shipping is one of the leading modes of transport that has dominated the world economy from past to present. The effectiveness and efficiency of maritime trade is closely related to maritime safety. Providing quality maritime safety is a fundamental requirement for environmentally friendly, sustainable, safe and efficient global trade. Therefore, maritime safety and human factors are frequently studied topics in literature. However, the fact that the human element has a complex socio-technical structure makes it difficult to fully analyse human factors in accidents. That is one of the biggest challenges in preventing and mitigating accidents. This research aims to demonstrate the feasibility of the modified Human Factor Analysis and Classification System for Passenger Vessel collisions (HFACS-PV) for other types of accidents. 51 grounding accidents that occurred in passenger vessels between 1991 and 2017 were analysed by using the HFACS-PV structure. The results show that the HFACS-PV structure created for passenger vessel accidents is compatible with contact, grounding and sinking accidents as well as collisions. Thus, the HFACS-PV structure allows for coherent analysis of marine accidents. Owing to the flexibility of HFACS, it is also possible to combine it with other analytical methods to conduct both qualitative and quantitative analysis.

Human and organizational factors within the public sectors for the prevention and control of epidemic

Pervasive human and organizational factors (HOFs) within the public sectors play a vital role in the prevention and control of epidemic (PCE). Insufficient analysis of HOFs has helped continue the use of flawed precautions. In this study, we attempted to establish a quantitative model to (a) clarify HOFs within the public sectors with regard to PCE, (b) predict the probability of relevant risk factors and an epidemic, and (c) diagnose the critical factors. First, we systematically identified 47 HOFs based on the Human Factors Analysis and Classification System (HFACS). We then converted the HFACS framework into a Bayesian Network (BN) after determining the causalities among these factors. Finally, we applied the hybrid HFACS-BN model to analyze the COVID-19 outbreak in China by virtue of its efficacy in probability prediction and diagnosis of key risk factors, and thus to test the feasibility of the model itself. This study contributes to a holistic analysis of HOFs within the public sectors with regard to PCE by providing a risk assessment model for epidemics or pandemics, and developing risk analysis methods for the public health field.

Challenges and Best Practices in Ethical Review of Human and Organizational Factors Studies in Health Technology: a Synthesis of Testimonies.

Summary Objective: Human and Organizational Factors (HOF) studies in health technology involve human beings and thus require Institutional Review Board (IRB) approval. Yet HOF studies have specific constraints and methods that may not fit standard regulations and IRB practices. Gaining IRB approval may pose difficulties for HOF researchers. This paper aims to provide a first overview of HOF study challenges to get IRB review by exploring differences and best practices across different countries. Methods: HOF researchers were contacted by email to provide a testimony about their experience with IRB review and approval. Testimonies were thematically analyzed and synthesized to identify and discuss shared themes. Results: Researchers from seven European countries, Argentina, Canada, Australia, and the United States answered the call. Four themes emerged that indicate shared challenges in legislation, IRB inefficiencies and inconsistencies, general regulation and costs, and lack of HOF study knowledge by IRB members. We propose a model for IRB review of HOF studies based on best practices. Conclusion: International criteria are needed that define low and high-risk HOF studies, to allow identification of studies that can undergo an expedited (or exempted) process from those that need full IRB review. Enhancing IRB processes in such a way would be beneficial to the conduct of HOF studies. Greater knowledge and promotion of HOF methods and evidence-based HOF study designs may support the evolving discipline. Based on these insights, training and guidance to IRB members may be developed to support them in ensuring that appropriate ethical issues for HOF studies are considered.

Investigation into the role of human and organizational factors in security work against terrorism at large-scale events

The pervasive human and organizational factors (HOFs) in security work at large-scale events (LSEs) contribute greatly to preventing terrorist attacks. However, as they have not been systematically analyzed, the formulation of relevant precautions could be flawed. This study aims to construct a quantitative model for: (i) systematically analyzing the HOFs in security work against terrorism at LSEs, (ii) predicting the probability of such terrorist attacks, and (iii) diagnosing the most critical HOFs. First, 30 HOFs were systematically identified by modifying the Human Factors Analysis and Classification System (HFACS) and integrating relevant historical data, literature, and expert knowledge. Second, these HOFs were statistically analyzed. Finally, by taking the Beijing 2022 Winter Olympics as an example, a hybrid HFACS-Bayesian Network model was constructed to quantitatively analyze the HOFs based on data collected through questionnaires and expert interviews. The example demonstrates the hybrid model’s capabilities in probability prediction and key factor diagnosis. This study contributes to the establishment of a systematic causation model for analyzing the root causes of the failure of security against terrorism at LSEs, which will enable more holistic incident investigation and more accurate formulation of precautions, as well as helping the development of risk analysis methods in the public security field.

ATEX-HOF Methodology: Innovation Driven by Human and Organizational Factors (HOF) in Explosive Atmosphere Risk Assessment

ATEX (explosive atmosphere) risk assessment is required when any equipment or system could generate a potentially explosive atmosphere. Despite the fact that many operations on plants and equipment containing dangerous substances are performed by operators, influences of human and organizational factors (HOF) are mostly neglected in the ATEX risk assessment. The integrated methodology described here is proposed to address two challenges: (1) identification of the HOF influence on the ATEX risk assessment, and (2) quantification of the HOF influence. The proposed methodology enriches the traditional ATEX risk assessment procedure, which consists of four steps: (1) area classification, (2) ignition source identification, (3) damage analysis, and (4) ATEX risk evaluation. The advantages of the ATEX-HOF methodology are demonstrated through the application to a paint mixing station in an automotive manufacturing plant. The ATEX risk assessment methodologies are mainly semi-quantitative. The ATEX-HOF methodology provides a quantitative analysis for the area classification and ignition source identification, and a semi-quantitative approach for the damage analysis. As a result, the ATEX-HOF risk evaluation becomes more accurate. An event tree-based probabilistic assessment has been introduced, considering both the technical barrier failure (Prtbf) and the human intervention in terms of human error probability (HEP). The case study allowed for demonstrating how taking HOFs into account is particularly important in companies where the safety culture is lower and consequently, the usual hypothesis of the correctness of operator intervention (in maintenance, normal operations, and emergency) could bring to non-conservative results.

A novel Fuzzy Bayesian Network approach for safety analysis of process systems; An application of HFACS and SHIPP methodology

Abstract Chemical process industries (CPI) are inherently hazardous complex systems where large inventory of extremely flammable and explosive chemicals are processed and stored in a highly congested process area. A reliable safety analysis method plays a significant role to measure risks and to develop preventive strategies in process industries. This paper proposed a novel Fuzzy Bayesian Network for dynamic safety analysis of process systems by incorporating Bayesian network (BN) with Fuzzy Best Worst Method (Fuzzy-BWM). In the proposed approach a comprehensive and in-depth analysis of human and organizational factors (HOFs) involving in the accident scenario occurrence was also provided by integrating Human Factor Analysis and Classification System (HFACS) and System Hazard Identification, Prediction and Prevention (SHIPP) methodology into the model. An ethylene storage tank was selected to verify the applicability of the proposed approach and its application potential. The study also explained a comparison between the results of the proposed Fuzzy-BWM approach with the conventional BN approach and a quantitative risk assessment (QRA) conventional technique such as bow-tie (BT). The findings revealed the capability of the proposed Fuzzy-BWM approach to provide high reliable results and to detect risks that using the BT and BN approaches were not identified.

Human and Organisational Factors Training as a Risk Management Strategy in an Aviation Maintenance Company

Aviation has for many years been one of the leading industries in addressing human and organisational factors (HOF) within its different sectors – flight operations, air traffic control, ground operations, maintenance, etc. In particular aviation has led the way in mandating a range of measures that address HOF issues – reporting systems, shift-handover procedures, etc (e.g. UK CAA, 2003). A key element of the regulation has been the mandating of initial and continuation training in HOF for virtually all personnel working in aviation maintenance. By contrast the development of practice and regulation of risk and safety management in aviation has lagged behind process, power and nuclear industries (Gambetti et al., 2012; Leva et al., 2012). ICAO published its requirements for Safety Management in 2009 (ICAO, 2009) and these are still being translated into regulations by local aviation authorities. For example the EASA regulations require implementation of Safety Management Systems (SMS) in airlines by 2013. As a consequence of this historical sequence – the development of HOF regulations prior to safety management regulations – organisations are facing the challenge of integrating two programmes with related objectives developed to meet the requirements of different regulations. HOF training in aviation maintenance, in the European context, normally comprises of a two day initial training classroom based workshop supplemented by a one-day continuation training workshop every 2 years. The continuation training typically comprises a refresher of key HOF concepts and information about company specific challenges. E-learning and blended learning are sometimes used for continuation training, but their acceptance by the local aviation authorities is variable. This paper reports an initiative to integrate HOF continuation training within a risk management context in an aviation maintenance company.