Error by design: Methods for predicting device usability

This paper introduces the idea of predicting ‘designer error’ by evaluating devices using Human Error Identification (HEI) techniques. This is demonstrated using Systematic Human Error Reduction and Prediction Approach (SHERPA) and Task Analysis For Error Identification (TAFEI) to evaluate a vending machine. Appraisal criteria which rely upon user opinion, face validity and utilisation are questioned. Instead a quantitative approach, based upon signal detection theory, is recommended. The performance of people using SHERPA and TAFEI are compared with heuristic judgement and each other. The results of these studies show that both SHERPA and TAFEI are better at predicting errors than the heuristic technique. The performance of SHERPA and TAFEI are comparable, giving some confidence in the use of these approaches. It is suggested that using HEI techniques as part of the design and evaluation process could help to make devices easier to use

Operationalising learning from rare events: framework for middle humanitarian operations managers

The purpose of this paper is to investigate the learning from rare events and the knowledge management processinvolved, which presents a significant challenge to many organizations. This is primarily attributed to the inability tointerpret these events in a systematic and “rich” manner, which this paper seeks to address. We start by summarizing therelevant literature on humanitarian operations management (HOM), outlining the evolution of the socio-technical disasterlifecycle and its relationship with humanitarian operations, using a supply chain resilience theoretical lens. We then out-line theories of organizational learning (and unlearning) from disasters and the impact on humanitarian operations. Subse-quently, we theorize the role of middle managers in humanitarian operations, which is the main focus of our paper. Themain methodology incorporates a hybrid of two techniques for root cause analysis, applied to two related case studies.The cases were specifically selected as, despite occurring twenty years apart, there are many similarities in the chain ofcausation and supporting factors, potentially suggesting that adequate learning from experience and failures is not occur-ring. This provides a novel learning experience within the HOM paradigm. Hence, the proposed approach is based on amultilevel structure that facilitates the operationalization of learning from rare events in humanitarian operations. Theresults show that we are able to provide an environment for multiple interpretations and effective learning, with emphasison middle managers within a humanitarian operations and crisis/disaster management context

A risk assessment approach to improve the resilience of a seaport system using Bayesian networks

Over the years, many efforts have been focused on developing methods to design seaport systems, yet disruption still occur because of various human, technical and random natural events. Much of the available data to design these systems are highly uncertain and difficult to obtain due to the number of events with vague and imprecise parameters that need to be modelled. A systematic approach that handles both quantitative and qualitative data, as well as means of updating existing information when new knowledge becomes available is required. Resilience, which is the ability of complex systems to recover quickly after severe disruptions, has been recognised as an important characteristic of maritime operations. This paper presents a modelling approach that employs Bayesian belief networks to model various influencing variables in a seaport system. The use of Bayesian belief networks allows the influencing variables to be represented in a hierarchical structure for collaborative design and modelling of the system. Fuzzy Analytical Hierarchy Process (FAHP) is utilised to evaluate the relative influence of each influencing variable. It is envisaged that the proposed methodology could provide safety analysts with a flexible tool to implement strategies that would contribute to the resilience of maritime systems

Identifying beliefs underlying pre-drivers’ intentions to take risks: an application of the theory of planned behaviour

Novice motorists are at high crash risk during the first few months of driving. Risky behaviours such as speeding and driving while distracted are well-documented contributors to crash risk during this period. To reduce this public health burden, effective road safety interventions need to target the pre-driving period. We use the Theory of Planned Behaviour (TPB) to identify the pre-driver beliefs underlying intentions to drive over the speed limit (N = 77), and while over the legal alcohol limit (N = 72), talking on a hand-held mobile phone (N = 77) and feeling very tired (N = 68). The TPB explained between 41% and 69% of the variance in intentions to perform these behaviours. Attitudes were strong predictors of intentions for all behaviours. Subjective norms and perceived behavioural control were significant, though weaker, independent predictors of speeding and mobile phone use. Behavioural beliefs underlying these attitudes could be separated into those reflecting perceived disadvantages (e.g., speeding increases my risk of crash) and advantages (e.g., speeding gives me a thrill). Interventions that can make these beliefs safer in pre-drivers may reduce crash risk once independent driving has begun

How can health care organisations make and justify decisions about risk reduction? Lessons from a cross-industry review and a health care stakeholder consensus development process

Interventions to reduce risk often have an associated cost. In UK industries decisions about risk reduction are made and justified within a shared regulatory framework that requires that risk be reduced as low as reasonably practicable. In health care no such regulatory framework exists, and the practice of making decisions about risk reduction is varied and lacks transparency. Can health care organisations learn from relevant industry experiences about making and justifying risk reduction decisions? This paper presents lessons from a qualitative study undertaken with 21 participants from five industries about how such decisions are made and justified in UK industry. Recommendations were developed based on a consensus development exercise undertaken with 20 health care stakeholders. The paper argues that there is a need in health care to develop a regulatory framework and an agreed process for managing explicitly the trade-off between risk reduction and cost. The framework should include guidance about a health care specific notion of acceptable levels of risk, guidance about standardised risk reduction interventions, it should include regulatory incentives for health care organisations to reduce risk, and it should encourage the adoption of an approach for documenting explicitly an organisation’s risk position

Protocol of an individual participant data meta-analysis to quantify the impact of high ambient temperatures on maternal and child health in Africa (HE 2 AT IPD)

Introduction: Globally, recognition is growing of the harmful impacts of high ambient temperatures (heat) on health in pregnant women and children. There remain, however, major evidence gaps on the extent to which heat increases the risks for adverse health outcomes, and how this varies between settings. Evidence gaps are especially large in Africa. We will conduct an individual participant data (IPD) meta-analysis to quantify the impacts of heat on maternal and child health in sub-Saharan Africa. A detailed understanding and quantification of linkages between heat, and maternal and child health is essential for developing solutions to this critical research and policy area. Methods and analysis: We will use IPD from existing, large, longitudinal trial and cohort studies, on pregnant women and children from sub-Saharan Africa. We will systematically identify eligible studies through a mapping review, searching data repositories, and suggestions from experts. IPD will be acquired from data repositories, or through collaboration with data providers. Existing satellite imagery, climate reanalysis data, and station-based weather observations will be used to quantify weather and environmental exposures. IPD will be recoded and harmonised before being linked with climate, environmental, and socioeconomic data by location and time. Adopting a one-stage and two-stage meta-analysis method, analytical models such as time-to-event analysis, generalised additive models, and machine learning approaches will be employed to quantify associations between exposure to heat and adverse maternal and child health outcomes. Ethics and dissemination: The study has been approved by ethics committees. There is minimal risk to study participants. Participant privacy is protected through the anonymisation of data for analysis, secure data transfer and restricted access. Findings will be disseminated through conferences, journal publications, related policy and research fora, and data may be shared in accordance with data sharing policies of the National Institutes of Health. PROSPERO registration number: CRD42022346068