Risk Ranking of Antimicrobial‐Resistant Hazards Found in Meat in Switzerland

Human exposure to bacteria resistant to antimicrobials and transfer of related genes is a complex issue and occurs, among other pathways, via meat consumption. In a context of limited resources, the prioritization of risk management activities is essential. Since the antimicrobial resistance (AMR) situation differs substantially between countries, prioritization should be country specific. The objective of this study was to develop a systematic and transparent framework to rank combinations of bacteria species resistant to selected antimicrobial classes found in meat, based on the risk they represent for public health in Switzerland. A risk assessment model from slaughter to consumption was developed following the Codex Alimentarius guidelines for risk analysis of foodborne AMR. Using data from the Swiss AMR monitoring program, 208 combinations of animal species/bacteria/antimicrobial classes were identified as relevant hazards. Exposure assessment and hazard characterization scores were developed and combined using multicriteria decision analysis. The effect of changing weights of scores was explored with sensitivity analysis. Attributing equal weights to each score, poultry‐associated combinations represented the highest risk. In particular, contamination with extended‐spectrum β‐lactamase/plasmidic AmpC‐producing Escherichia coli in poultry meat ranked high for both exposure and hazard characterization. Tetracycline‐ or macrolide‐resistant Enterococcus spp., as well as fluoroquinolone‐ or macrolide‐resistant Campylobacter jejuni , ranked among combinations with the highest risk. This study provides a basis for prioritizing future activities to mitigate the risk associated with foodborne AMR in Switzerland. A user‐friendly version of the model was provided to risk managers; it can easily be adjusted to the constantly evolving knowledge on AMR.     

Risk Perception of Heavy Metal Soil Contamination and Attitudes toward Decontamination Strategies

Contaminated soils are a common environmental risk all over the world. One major source of risk is heavy metal soil contamination caused by industrial emissions. This quasiexperimental study investigated the perception of these risks by exposed and nonexposed people, their attitudes toward bioremediation methods using hyperaccumulating plants, and the influence of long-term aspects of sustainability on the acceptance of bioremediation methods. Major findings were that people living in a contaminated area perceived the risk of the heavy metal soil contamination as higher than the general risk of contamination. Second, a factor analysis showed that the factors dread, control, and catastrophic potential were relevant for the perception and valuation of low-dose environmental risks such as the contamination of the investigated area. In addition, a cluster analysis showed that the risk of heavy metal soil contamination was perceived as similar to that of oil contamination, ozone layer, preservatives and genetic technology. It was perceived indifferently with regard to dread. The uncontrollability of heavy metal soil contamination was estimated as medium, and its catastrophic potential as low. Third, exposed and nonexposed participants preferred bioremediation methods to classical methods (e.g., excavation and chemical treatment of the soil), because they perceived the environmental and esthetical performance of the bioremediation as important criteria. Sustainability or precautionary issues, such as the prevention of harm for future generations, were highly correlated with the acceptance of the use of bioremediation methods in people's residential areas.     

Sensitivity Analysis for Dependent Variables*

Decision analysts use sensitivity analysis to identify influential variables, to determine which input variables to model stochastically, and to characterize scenarios that could affect a change in the rank ordering of the alternatives. A frequently recommended sensitivity analysis technique is “one‐way” sensitivity analysis, which determines a variable's influence by the degree to which the objective function changes as that variable is varied while all other variables are held fixed. Disadvantages of one‐way analysis are that it measures the influence of only one variable at a time and it assumes independence among the input variables. Clearly, however, there are situations when dependencies exist among the input variables that could possibly affect the sensitivity analysis results. This research develops a strategy that incorporates dependence relations among the input variables into the sensitivity analysis using rank correlations. Only decision problems with a finite number of alternatives and continuous state variables are considered.     

Quantitative microbiological risk assessment of nontyphoidal Salmonella in ground pork in households in Chengdu,China

Foodborne disease caused by nontyphoidal Salmonella (NTS) is one of the most important food safety issues worldwide. The objectives of this study were to carry out microbial monitoring on the prevalence of NTS in commercial ground pork, investigate consumption patterns, and conduct a quantitative microbiological risk assessment (QMRA) that considers cross-contamination to determine the risk caused by consuming ground pork and ready-to-eat food contaminated during food handling in the kitchen in Chengdu, China. The food pathway of ground pork was simplified and assumed to be several units according to the actual situation and our survey data, which were collected from our research or references and substituted into the QMRA model for simulation. The results showed that the prevalence of NTS in ground pork purchased in Chengdu was 69.64% (95% confidence interval [CI], 60.2–78.0), with a mean contamination level of −0.164 log CFU/g. After general cooking, NTS in ground pork could be eliminated (contamination level of zero). The estimated probability of causing salmonellosis per day was 9.43E-06 (95% CI: 8.82E-06–1.00E-05), while the estimated salmonellosis cases per million people per year were 3442 (95% CI: 3218–3666). According to the sensitivity analysis, the occurrence of cross-contamination was the most important factor affecting the probability of salmonellosis. To reduce the risk of salmonellosis caused by NTS through ground pork consumption, reasonable hygiene prevention and control measures should be adopted during food preparation to reduce cross-contamination. This study provides valuable information for household cooking and food safety management in China.     

Realizing the Need for Rework: From Task Interdependence to Social Networks

Design rework is a core phenomenon in new product development (NPD). Yet carrying out design rework presupposes recognizing the need for it. I characterize the types of interpersonal knowledge transfer that help developers realize the need for design rework in NPD. As predicted by the NPD literature, I find that individuals who interact frequently with colleagues to address their task interdependences are more likely to realize the need for rework. I also learn that interacting with colleagues who have different expertise in process‐related knowledge (as opposed to product‐related knowledge) facilitates realizing the need for rework. However, to develop a deeper understanding of how individuals recognize the need for rework when interacting with others, we must expand our views beyond task interdependence and expertise‐related factors. In particular, organizational variables—both formal and informal—play a significant role. With respect to formal hierarchical structures, actors of superior rank are less likely to realize the need for rework regardless of whether or not their interacting partner is of superior rank; however, actors of superior rank are more likely to trigger realizing the need for rework when interacting with partners of subordinate rank. By examining an organization's informal structure, I discover that the social “embeddedness” of developers (i.e., the energy and attention invested in a dyadic relationship) significantly influences their propensity to realize the need for rework. Several hypotheses are tested in a sociometric study conducted within the development department of a software company, and I discuss the implications for behavioral operations in NPD.     

Leveraging risk assessment for foodborne outbreak investigations: The Quantitative Risk Assessment-Epidemic Curve Prediction Model

Root cause analysis can be used in foodborne illness outbreak investigations to determine the underlying causes of an outbreak and to help identify actions that could be taken to prevent future outbreaks. We developed a new tool, the Quantitative Risk Assessment-Epidemic Curve Prediction Model (QRA-EC), to assist with these goals and applied it to a case study to investigate and illustrate the utility of leveraging quantitative risk assessment to provide unique insights for foodborne illness outbreak root cause analysis. We used a 2019 Salmonella outbreak linked to melons as a case study to demonstrate the utility of this model (Centers for Disease Control and Prevention [CDC], 2019). The model was used to evaluate the impact of various root cause hypotheses (representing different contamination sources and food safety system failures in the melon supply chain) on the predicted number and timeline of illnesses. The predicted number of illnesses varied by contamination source and was strongly impacted by the prevalence and level of Salmonella contamination on the surface/inside of whole melons and inside contamination niches on equipment surfaces. The timeline of illnesses was most strongly impacted by equipment sanitation efficacy for contamination niches. Evaluations of a wide range of scenarios representing various potential root causes enabled us to identify which hypotheses, were likely to result in an outbreak of similar size and illness timeline to the 2019 Salmonella melon outbreak. The QRA-EC framework can be adapted to accommodate any food–pathogen pairs to provide insights for foodborne outbreak investigations.     

Quantitative Risk Assessment of Human Salmonellosis in Canadian Broiler Chicken Breast from Retail to Consumption

The current quantitative risk assessment model followed the framework proposed by the Codex Alimentarius to provide an estimate of the risk of human salmonellosis due to consumption of chicken breasts which were bought from Canadian retail stores and prepared in Canadian domestic kitchens. The model simulated the level of Salmonella contamination on chicken breasts throughout the retail‐to‐table pathway. The model used Canadian input parameter values, where available, to represent risk of salmonellosis. From retail until consumption, changes in the concentration of Salmonella on each chicken breast were modeled using equations for growth and inactivation. The model predicted an average of 318 cases of salmonellosis per 100,000 consumers per year. Potential reasons for this overestimation were discussed. A sensitivity analysis showed that concentration of Salmonella on chicken breasts at retail and food hygienic practices in private kitchens such as cross‐contamination due to not washing cutting boards (or utensils) and hands after handling raw meat along with inadequate cooking contributed most significantly to the risk of human salmonellosis. The outcome from this model emphasizes that responsibility for protection from Salmonella hazard on chicken breasts is a shared responsibility. Data needed for a comprehensive Canadian Salmonella risk assessment were identified for future research.     

Prioritizing Environmental Health Risks in the UAE

This article presents the results of a comparative environmental risk‐ranking exercise that was conducted in the United Arab Emirates (UAE) to inform a strategic planning process led by the Environment Agency‐Abu Dhabi (EAD). It represents the first national‐level application of a deliberative method for comparative risk ranking first published in this journal. The deliberative method involves a five‐stage process that includes quantitative risk assessment by experts and deliberations by groups of stakeholders. The project reported in this article considered 14 categories of environmental risks to health identified through discussions with EAD staff: ambient and indoor air pollution; drinking water contamination; coastal water pollution; soil and groundwater contamination; contamination of fruits, vegetables, and seafood; ambient noise; stratospheric ozone depletion; electromagnetic fields from power lines; health impacts from climate change; and exposure to hazardous substances in industrial, construction, and agricultural work environments. Results from workshops involving 73 stakeholders who met in five separate groups to rank these risks individually and collaboratively indicated strong consensus that outdoor and indoor air pollution are the highest priorities in the UAE. Each of the five groups rated these as being among the highest risks. All groups rated soil and groundwater contamination as being among the lowest risks. In surveys administered after the ranking exercises, participants indicated that the results of the process represented their concerns and approved of using the ranking results to inform policy decisions. The results ultimately shaped a strategic plan that is now being implemented.     

A Practical Framework for the Construction of a Biotracing Model: Application to Salmonella in the Pork Slaughter Chain

A novel purpose of the use of mathematical models in quantitative microbial risk assessment (QMRA) is to identify the sources of microbial contamination in a food chain (i.e., biotracing). In this article we propose a framework for the construction of a biotracing model, eventually to be used in industrial food production chains where discrete numbers of products are processed that may be contaminated by a multitude of sources. The framework consists of steps in which a Monte Carlo model, simulating sequential events in the chain following a modular process risk modeling (MPRM) approach, is converted to a Bayesian belief network (BBN). The resulting model provides a probabilistic quantification of concentrations of a pathogen throughout a production chain. A BBN allows for updating the parameters of the model based on observational data, and global parameter sensitivity analysis is readily performed in a BBN. Moreover, a BBN enables “backward reasoning” when downstream data are available and is therefore a natural framework for answering biotracing questions. The proposed framework is illustrated with a biotracing model of Salmonella in the pork slaughter chain, based on a recently published Monte Carlo simulation model. This model, implemented as a BBN, describes the dynamics of Salmonella in a Dutch slaughterhouse and enables finding the source of contamination of specific carcasses at the end of the chain.     

Probabilistic Modeling of the Fate of Listeria Monocytogenes in Diced Bacon During the Manufacturing Process

To assess the impact of the manufacturing process on the fate of Listeria monocytogenes, we built a generic probabilistic model intended to simulate the successive steps in the process. Contamination evolution was modeled in the appropriate units (breasts, dice, and then packaging units through the successive steps in the process). To calibrate the model, parameter values were estimated from industrial data, from the literature, and based on expert opinion. By means of simulations, the model was explored using a baseline calibration and alternative scenarios, in order to assess the impact of changes in the process and of accidental events. The results are reported as contamination distributions and as the probability that the product will be acceptable with regards to the European regulatory safety criterion. Our results are consistent with data provided by industrial partners and highlight that tumbling is a key step for the distribution of the contamination at the end of the process. Process chain models could provide an important added value for risk assessment models that basically consider only the outputs of the process in their risk mitigation strategies. Moreover, a model calibrated to correspond to a specific plant could be used to optimize surveillance.     

A Stochastic Model to Assess the Effect of Meat Inspection Practices on the Contamination of the Pig Carcasses

The objective of meat inspection is to promote animal and public health by preventing, detecting, and controlling hazards originating from animals. With the improvements of sanitary level in pig herds, the hazards profile has shifted and the inspection procedures no longer target major foodborne pathogens (i.e., not risk based). Additionally, carcass manipulations performed when searching for macroscopic lesions can lead to cross‐contamination. We therefore developed a stochastic model to quantitatively describe cross‐contamination when consecutive carcasses are submitted to classic inspection procedures. The microbial hazard used to illustrate the model was Salmonella, the data set was obtained from Brazilian slaughterhouses, and some simplifying assumptions were made. The model predicted that due to cross‐contamination during inspection, the prevalence of contaminated carcass surfaces increased from 1.2% to 95.7%, whereas the mean contamination on contaminated surfaces decreased from 1 logCFU/cm² to ?0.87 logCFU/cm², and the standard deviations decreased from 0.65 to 0.19. These results are explained by the fact that, due to carcass manipulations with hands, knives, and hooks, including the cutting of contaminated lymph nodes, Salmonella is transferred to previously uncontaminated carcasses, but in small quantities. These small quantities can easily go undetected during sampling. Sensitivity analyses gave insight into the model performance and showed that the touching and cutting of lymph nodes during inspection can be an important source of carcass contamination. The model can serve as a tool to support discussions on the modernization of pig carcass inspection.     

Sensitivity Analysis of a Two-Dimensional Probabilistic Risk Assessment Model Using Analysis of Variance

This article demonstrates application of sensitivity analysis to risk assessment models with two-dimensional probabilistic frameworks that distinguish between variability and uncertainty. A microbial food safety process risk (MFSPR) model is used as a test bed. The process of identifying key controllable inputs and key sources of uncertainty using sensitivity analysis is challenged by typical characteristics of MFSPR models such as nonlinearity, thresholds, interactions, and categorical inputs. Among many available sensitivity analysis methods, analysis of variance (ANOVA) is evaluated in comparison to commonly used methods based on correlation coefficients. In a two-dimensional risk model, the identification of key controllable inputs that can be prioritized with respect to risk management is confounded by uncertainty. However, as shown here, ANOVA provided robust insights regarding controllable inputs most likely to lead to effective risk reduction despite uncertainty. ANOVA appropriately selected the top six important inputs, while correlation-based methods provided misleading insights. Bootstrap simulation is used to quantify uncertainty in ranks of inputs due to sampling error. For the selected sample size, differences in F values of 60% or more were associated with clear differences in rank order between inputs. Sensitivity analysis results identified inputs related to the storage of ground beef servings at home as the most important. Risk management recommendations are suggested in the form of a consumer advisory for better handling and storage practices.     

Analysing Wage Differences between the USA and Germany Using Proportional Hazards Models

We analyse differences between the wage distributions in the USA and Germany in 2001 for both women and men. The empirical analysis is based on the decomposition of differences using Cox's marginal (partial) likelihood. The approach based on rank invariant estimators such as Cox's is borrowed from the literature on failure time data. Donald et al. pioneered this approach in 2000. However, they did not use the full power of the semi‐parametric approach. Instead, they argued for using a piecewise constant hazard rate model. We improve on their work by showing that the semi‐parametric features of Cox's marginal likelihood are as appropriate for the analysis of wage decompositions and as easy to interpret. Moreover, we extend their approach by allowing for non‐linear regression effects. We show empirically that this formulation both increases the flexibility of their approach and improves the discriminatory power between wage regimes.     

Salmonella Prevalence Alone Is Not a Good Indicator of Poultry Food Safety

Salmonella is a leading cause of foodborne illness (i.e., salmonellosis) outbreaks, which on occasion are attributed to ground turkey. The poultry industry uses Salmonella prevalence as an indicator of food safety. However, Salmonella prevalence is only one of several factors that determine risk of salmonellosis. Consequently, a model for predicting risk of salmonellosis from individual lots of ground turkey as a function of Salmonella prevalence and other risk factors was developed. Data for Salmonella contamination (prevalence, number, and serotype) of ground turkey were collected at meal preparation. Scenario analysis was used to evaluate effects of model variables on risk of salmonellosis. Epidemiological data were used to simulate Salmonella serotype virulence in a dose‐response model that was based on human outbreak and feeding trial data. Salmonella prevalence was 26% (n = 100) per 25 g of ground turkey, whereas Salmonella number ranged from 0 to 1.603 with a median of 0.185 log per 25 g. Risk of salmonellosis (total arbitrary units (AU) per lot) was affected (p ≤ 0.05) by Salmonella prevalence, number, and virulence, by incidence and extent of undercooking, and by food consumption behavior and host resistance but was not (p > 0.05) affected by serving size, serving size distribution, or total bacterial load of ground turkey when all other risk factors were held constant. When other risk factors were not held constant, Salmonella prevalence was not correlated (r = ?0.39; p = 0.21) with risk of salmonellosis. Thus, Salmonella prevalence alone was not a good indicator of poultry food safety because other factors were found to alter risk of salmonellosis. In conclusion, a more holistic approach to poultry food safety, such as the process risk model developed in the present study, is needed to better protect public health from foodborne pathogens like Salmonella.     

Interventions Targeting Deep Tissue Lymph Nodes May Not Effectively Reduce the Risk of Salmonellosis from Ground Pork Consumption: A Quantitative Microbial Risk Assessment

The inclusion of deep tissue lymph nodes (DTLNs) or nonvisceral lymph nodes contaminated with Salmonella in wholesale fresh ground pork (WFGP) production may pose risks to public health. To assess the relative contribution of DTLNs to human salmonellosis occurrence associated with ground pork consumption and to investigate potential critical control points in the slaughter‐to‐table continuum for the control of human salmonellosis in the United States, a quantitative microbial risk assessment (QMRA) model was established. The model predicted an average of 45 cases of salmonellosis (95% CI = [19, 71]) per 100,000 Americans annually due to WFGP consumption. Sensitivity analysis of all stochastic input variables showed that cooking temperature was the most influential parameter for reducing salmonellosis cases associated with WFGP meals, followed by storage temperature and Salmonella concentration on contaminated carcass surface before fabrication. The input variables were grouped to represent three main factors along the slaughter‐to‐table chain influencing Salmonella doses ingested via WFGP meals: DTLN‐related factors, factors at processing other than DTLNs, and consumer‐related factors. The evaluation of the impact of each group of factors by second‐order Monte Carlo simulation showed that DTLN‐related factors had the lowest impact on the risk estimate among the three groups of factors. These findings indicate that interventions to reduce Salmonella contamination in DTLNs or to remove DTLNs from WFGP products may be less critical for reducing human infections attributable to ground pork than improving consumers’ cooking habits or interventions of carcass decontamination at processing.     

Predictors of Parental Risk Perceptions: The Case of Child Pedestrian Injuries in School Context

The objective of this article is to explore the factors that influence parental risk perceptions of child pedestrian injuries in the elementary school context. Parents (n= 193) from six different schools responded to a questionnaire on road safety, including a measure of their risk perception. Results of bivariate analyses show that eight variables are significantly related to risk perception. Environmental variables, as we measure them, were not significant, contrary to our initial hypotheses. Only three variables, parent's gender, perceived primary source of danger, and sense of control remained significant in OLS regression analyses (adjusted R² of 0.16, F= 9.27; p= 0.00). Since parents’ perceptions of road risks are an important factor in their road safety practices and in their choice of transportation mode used for their child's journey to school, our analysis elucidates factors underlying these choices. Our results can help decisionmakers to design traffic injury prevention measures and to promote physical activity through the use of active modes of transport.     

Classification of Listeria monocytogenes Persistence in Retail Delicatessen Environments Using Expert Elicitation and Machine Learning

Increasing evidence suggests that persistence of Listeria monocytogenes in food processing plants has been the underlying cause of a number of human listeriosis outbreaks. This study extracts criteria used by food safety experts in determining bacterial persistence in the environment, using retail delicatessen operations as a model. Using the Delphi method, we conducted an expert elicitation with 10 food safety experts from academia, industry, and government to classify L. monocytogenes persistence based on environmental sampling results collected over six months for 30 retail delicatessen stores. The results were modeled using variations of random forest, support vector machine, logistic regression, and linear regression; variable importance values of random forest and support vector machine models were consolidated to rank important variables in the experts’ classifications. The duration of subtype isolation ranked most important across all expert categories. Sampling site category also ranked high in importance and validation errors doubled when this covariate was removed. Support vector machine and random forest models successfully classified the data with average validation errors of 3.1% and 2.2% (n = 144), respectively. Our findings indicate that (i) the frequency of isolations over time and sampling site information are critical factors for experts determining subtype persistence, (ii) food safety experts from different sectors may not use the same criteria in determining persistence, and (iii) machine learning models have potential for future use in environmental surveillance and risk management programs. Future work is necessary to validate the accuracy of expert and machine classification against biological measurement of L. monocytogenes persistence.     

A Mathematical Model for Pathogen Cross‐Contamination Dynamics during the Postharvest Processing of Leafy Greens

We developed a probabilistic mathematical model for the postharvest processing of leafy greens focusing on Escherichia coli O157:H7 contamination of fresh‐cut romaine lettuce as the case study. Our model can (i) support the investigation of cross‐contamination scenarios, and (ii) evaluate and compare different risk mitigation options. We used an agent‐based modeling framework to predict the pathogen prevalence and levels in bags of fresh‐cut lettuce and quantify spread of E. coli O157:H7 from contaminated lettuce to surface areas of processing equipment. Using an unbalanced factorial design, we were able to propagate combinations of random values assigned to model inputs through different processing steps and ranked statistically significant inputs with respect to their impacts on selected model outputs. Results indicated that whether contamination originated on incoming lettuce heads or on the surface areas of processing equipment, pathogen prevalence among bags of fresh‐cut lettuce and batches was most significantly impacted by the level of free chlorine in the flume tank and frequency of replacing the wash water inside the tank. Pathogen levels in bags of fresh‐cut lettuce were most significantly influenced by the initial levels of contamination on incoming lettuce heads or surface areas of processing equipment. The influence of surface contamination on pathogen prevalence or levels in fresh‐cut bags depended on the location of that surface relative to the flume tank. This study demonstrates that developing a flexible yet mathematically rigorous modeling tool, a “virtual laboratory,” can provide valuable insights into the effectiveness of individual and combined risk mitigation options.     

Environmental Exposure of the Adult French Population to Permethrin

This work aims to assess the exposure to permethrin of the adult French population from available contamination measurements of outdoor air, indoor air, and settled dust. Priority is given to the assessment of chronic exposure, given the potential of permethrin to induce cancers and/or endocrine disorders. A statistical method was devised to calculate exposure to permethrin by different pathways (inhalation, indirect dust ingestion, and dermal contact). This method considers anthropometric parameters, the population's space–time budget, and recent methods for calculating dermal exposure. Considering the media of interest, our results pointed to house dust as the main environmental source of permethrin exposure, followed by indoor and outdoor air. Dermal contact and indirect dust ingestion may be more important exposure pathways than inhalation. A sensitivity analysis indicated that exposure estimates were mainly affected by variability within contamination data. This study is the first step in aggregated exposure and risk assessment due to pyrethroid exposure. Outdoor air, indoor air, and settled dust may constitute significant exposure sources, in addition to diet, which could be important. The next step entails assessing internal doses and estimating the proportion of each exposure source and pathway relative to internal exposure.     

Emergency risk communication and sensemaking during smoke events: A survey of practitioners

Emergency risk communication (ERC) for smoke emitted from major fires continues to challenge governments. During these events, practitioners (including scientific, communication, and emergency response government staff) are tasked with quickly making sense of the public health risks and the communication options available. Practitioners’ sensemaking—the process of creating meaning from information about an unfolding emergency—is key to effective ERC. This article identifies the factors that ERC practitioners consider the most important to their sensemaking for smoke events. A survey of practitioners (n = 86) was conducted to elicit their views on the level of importance of 22 different factors (individual, organizational, and contextual) on their sensemaking. The results indicate that the majority of the factors tested are very important to practitioners. This finding likely reflects the multidimensional nature of emergency smoke events and provides evidence as to why practitioners are challenged when trying to make sense of emergency situations. Despite multiple factors being considered very important to practitioners, the time-limited nature of emergencies means that practitioners will inevitability be forced to prioritize in their sensemaking efforts. Our results also provide insight into practitioners’ prioritization of different information sources. Specifically, practitioners prioritize their own knowledge and the knowledge of other practitioners. The two most important factors were information from other incident management stakeholders and the practitioners’ past experience. Other information, including community-based and academic knowledge, appear to be of lower priority for practitioners. Based on the study results, recommendations for practice and future research are discussed.