Quantitative Measure Of Risk Research Articles

Risk Analysis of Electricity Demand at Public Electric Vehicle Charging Stations (SPKLU): CVaR Model Approach

The electricity demand at Public Electric Vehicle Charging Stations (SPKLUs) exhibits significant volatility, which is driven by several aspects including electricity demand patterns at specific time intervals, load variability, SPKLU capacity, and other related factors. The variability of these swings can present hazards for SPKLU operators in relation to energy administration as well as operational and financial hazards. The objective of this study is to assess the risk associated with energy demand fluctuation at SPKLUs by employing the Conditional Value-at-Risk (CVaR) model technique. CVaR, or Conditional Value at Risk, is a quantitative measure of risk that calculates the predicted loss value in the most unfavorable situation. It is commonly employed to enhance the risk management approach of SPKLUs. The electricity demand at SPKLU exhibits significant volatility, with an average fluctuation of 10.15% and a standard deviation of 49.67%. The CVAR, calculated at -121.19% for a confidence interval of 1%, represents the maximum potential loss that could be experienced during worst-case electrical demand conditions, highlighting the substantial fluctuations in demand. The study initially implemented the CVaR model to analyze power demand at SPKLU, providing novel perspectives on risk reduction for critical infrastructure and proposing unique strategies for managing demand fluctuations in a reliable and efficient manner. The results also offer comprehensive insights into risk exposure and facilitate the formulation of well-informed and strategic risk management plans.

Multisite external validation of blood test for colorectal neoplasia screening in a majority average-risk screening cohort of 449 subjects.

66 Background: The FirstSight CRC screening blood test was previously shown to have sensitivities of 92.1% and 54.5% for CRC and AA at a specificity of 90.6% for 1,038 subjects from 18 clinical sites. In this report, we describe the independent external validation of the predefined FirstSight CRC screening blood test with new subjects from 16 sites dispersed across the US, 4 of which are new and did not participate in the previous study. Methods: The validation study included subjects from two sources: Average-risk, asymptomatic screening subjects from screening clinics supplemented with diseased-only subjects from surgical oncology centers. Blood was drawn before bowel prep for colonoscopy or treatment. External validation as well as Monte Carlo cross-validation (MCCV) methods were used to evaluate the performance of the pre-defined FirstSight assay, algorithm, and clinical thresholds in this independent external validation set. Results include sensitivity and specificity to detect CRC and AA and 95% confidence intervals. Results: The study cohort (55.2% female; mean age 56.9 yrs.) consisted of 449 subjects (White 61.7%; Black 14.5%; Hispanic 18%; AAPI and Middle Easterners 5.8%). The majority of them, 376 (84%), were asymptomatic, average-risk screening subjects (including 1 CRC, 53 AA, and all 322 negative subjects). An additional 73 (16%) diseased subjects (39 CRC and 34 AA) were enriched due to low disease prevalence. The FirstSight algorithm derives a test score from 0 to 100 as a quantitative measure of AA and CRC risk with a cutpoint to assign a binary low- or high-risk designation. The previously described cutpoint of 47.2 yielded a test specificity of 90.0%, and 90.0% and 52.9% sensitivity for the detection of CRC and AA, respectively. Here we report a higher cutpoint 50 with identical sensitivities but improved specificity to 90.7%. Point estimates of sensitivity and specificity, including CRC staging, and Clopper-Pearson exact 95% confidence intervals based on the external validation results are presented below. Conclusions: An external validation included 449 subjects from a multi-site, majority average-risk CRC screening study successfully validated the FirstSight Blood Test’s high performance as previously established with 1,038 subjects. The results bring forth confidence in the Test’s reproducibility in a large-scale clinical trial. Clinical trial information: NCT05127096 . [Table: see text]

A Component Expected Shortfall Approach to Systemic Risk: An Application in the South African Financial Industry

The accelerated growth and interconnectedness of financial institutions and movement towards products and activities outside the regulatory purview have been met with huge concerns. South Africa is one of the emerging economies that this conundrum has beset. Any potential instability in the financial sector likely poses insurmountable consequences and unprecedented government intervention, especially given that the country currently has no deposit insurance scheme. Although it is easy to justify the channels through which banks contribute to destabilising financial markets, it remains a controversial issue for insurers and other non-banking institutions. This study aims to empirically quantify the contribution of banks and insurers to aggregate the systemic risk of their respective industries by employing the component expected shortfall (CES). The CES is a robust quantitative systemic risk measure that allows for a comprehensive assessment of systemic risk by considering the contributions of individual financial components. Our findings demonstrate that the rankings from the CES framework are closely aligned with the regulatory D-SIB surcharges of the banking entities included in the study. The close alignment of both approaches is primarily due to the consideration of the size of an institution, amongst other factors.

Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk

Flood risk assessment is a key step in flood management and mitigation, and flood risk maps provide a quantitative measure of flood risk. Therefore, integration of deep learning – an updated version of machine learning techniques – and multi-criteria decision making (MCDM) models can generate high-resolution flood risk maps. In this study, a novel integrated approach has been developed based on multiplicative long short-term memory (mLSTM) deep learning models and an MCDM ensemble model to map flood risk in the Minab-Shamil plain, southern Iran. A flood hazard map generated by the mLSTM model is based on nine critical features selected by GrootCV (distance to the river, vegetation cover, variables extracted from DEM (digital elevation model) and river density) and a flood inventory map (70% and 30% data were randomly selected as training and test datasets, respectively). The values of all criteria used to assess model accuracy performance (except Cohens kappa for train dataset = 86, and for test dataset = 84) achieved values greater than 90, which indicates that the mLSTM model performed very well for the generation of a spatial flood hazard map. According to the spatial flood hazard map produced by mLSTM, the very low, low, moderate, high and very high classes cover 26%, 35.3%, 20.5%, 11.2% and 7% of the total area, respectively. Flood vulnerability maps were produced by the combinative distance-based assessment (CODAS), the evaluation based on distance from average solution (EDAS), and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA), and then validated by Spearman's rank correlation coefficients (SRC). Based on the SRC, the three models CODAS, EDAS, and MOOSRA showed high-ranking correlations with each other, and all three models were then used in the ensemble process. According to the CODAS-EDAS-MOOSRA ensemble model, 21.5%, 34.2%, 23.7%, 13%, and 7.6% of the total area were classified as having a very low to very high flood vulnerability, respectively. Finally, a flood risk map was generated by the combination of flood hazard and vulnerability maps produced by the mLSTM and MCDM ensemble model. According to the flood risk map, 27.4%, 34.3%, 14.8%, 15.7%, and 7.8% of the total area were classified as having a very low, low, moderate, high, and very high flood risk, respectively. Overall, the integration of mLSTM and the MCDM ensemble is a promising tool for generating precise flood risk maps and provides a useful reference for flood risk management.

Explainable Artificial Intelligence to Identify Dosimetric Predictors of Toxicity in Patients with Locally Advanced Non-Small Cell Lung Cancer: A Secondary Analysis of RTOG 0617

Dosimetric predictors of toxicity in patients treated with definitive chemoradiation for locally advanced non-small cell lung cancer are often identified through trial and error. This study used machine learning (ML) and explainable artificial intelligence to empirically characterize dosimetric predictors of toxicity in patients treated as part of a prospective clinical trial. A secondary analysis of the Radiation Therapy Oncology Group (RTOG) 0617 trial was performed. Multiple ML models were trained to predict grade ≥3 pulmonary, cardiac, and esophageal toxicities using clinical and dosimetric features. Model performance was evaluated using the area under the curve (AUC). The best performing model for each toxicity was explained using the Shapley Additive Explanation (SHAP) framework; SHAP values were used to identify relevant dosimetric thresholds and were converted to odds ratios (ORs) with confidence intervals (CIs) generated using bootstrapping to obtain quantitative measures of risk. Thresholds were validated using logistic regression. The best-performing models for pulmonary, cardiac, and esophageal toxicities, outperforming logistic regression, were extreme gradient boosting (AUC, 0.739), random forest (AUC, 0.706), and naive Bayes (AUC, 0.721), respectively. For pulmonary toxicity, thresholds of a mean dose >18 Gy (OR, 2.467; 95% CI, 1.049-5.800; P=.038) and lung volume receiving ≥20 Gy (V20) > 37% (OR, 2.722; 95% CI, 1.034-7.163; P=.043) were identified. For esophageal toxicity, thresholds of a mean dose >34 Gy (OR, 4.006; 95% CI, 2.183-7.354; P < .001) and V20 > 37% (OR, 3.725; 95% CI, 1.308-10.603; P=.014) were identified. No significant thresholds were identified for cardiac toxicity. In this data set, ML approaches validated known dosimetric thresholds and outperformed logistic regression at predicting toxicity. Furthermore, using explainable artificial intelligence, clinically useful dosimetric thresholds might be identified and subsequently externally validated.

Characteristics of ecological risks in ecosystems and their manifestation possibility assessment

Environment pollution with ecotoxicants of natural and anthropogenic origin creates risks of negative impact on the state of the environment, the biota of ecosystems, including humans. The study defnes the main sources of ecotoxic pollutants and analyzes the risks that may arise. Based on the ambiguity of the "risk" term defnition, the interpretation of the term is discussed. The approach to defning risk as the probability of certain adverse events or processes taking into account possible consequences is noted. The relationship between environmental risks and the reliability of ecosystems is indicated. Environmental risk assessment is a complex process that involves identifying the likelihood of reversible or irreversible changes in the structure and functions of ecosystems in response to anthropogenic or natural impacts. The quantitative measure of risk as a threat of danger is the product of the probability of an adverse outcome of the event and the expected loss as a result of the event. Considerable attention is paid to the assessment of possible ecological risks for agroecosystems, which are associated with environmental degradation, the impact on human health, and the possible ingress of toxic substances into food products and pet fodder. Ecological risks in agroecosystems arise, in particular, as a result of the use of plant protection products (pesticides), excessive use of agrochemicals. However, the process of risk management in agroecosystems involves the selection and use of risk reduction methods, in particular, the use of measures to regulate the impact of ecotoxicants by limiting, banning the most toxic and preventing pollution. The study of environmental risk is necessary to understand the impact of harmful factors on environmental components. It is emphasized that risk management in agriculture should be aimed at preserving the agroecosystem optimal state and protecting human health. Key words: environmental risk, environment, ecotoxicants, rating, danger, agroecosystem, pesticides.

Food anaphylaxis diagnostic marker compilation in machine learning design and validation.

Traditional food allergy assessment of anaphylaxis remains limited in accuracy and accessibility. Current methods of anaphylaxis risk assessment are costly with low predictive accuracy. The Tolerance Induction Program (TIP) for anaphylactic patients undergoing TIP immunotherapy produced large-scale diagnostic data across biosimilar proteins, which was used to develop a machine learning model for patient-specific and allergen-specific anaphylaxis assessment. In explanation of construct, this work describes the algorithm design for assignment of peanut allergen score as a quantitative measure of anaphylaxis risk. Secondarily, it confirms the accuracy of the machine learning model for a specific cohort of food anaphylactic children. Machine learning model design for allergen score prediction utilized 241 individual allergy assays per patient. Accumulation of data across total IgE subdivision served as the basis of data organization. Two regression based Generalized Linear Models (GLM) were utilized to position allergy assessment on a linear scale. The initial model was further tested with sequential patient data over time. A Bayesian method was then used to improve outcomes by calculating the adaptive weights for the results of the two GLMs of peanut allergy score prediction. A linear combination of both provided the final hybrid machine learning prediction algorithm. Specific analysis of peanut anaphylaxis within one endotype model is estimated to predict the severity of possible anaphylactic reaction to peanut with a recall of 95.2% on a dataset of 530 juvenile patients with various food allergies, including but not limited to peanut allergy. Receiver Operating Characteristic analysis yielded over 99% AUC (area under curve) results within peanut allergy prediction. Machine learning algorithm design established from comprehensive molecular allergy data produces high accuracy and recall in anaphylaxis risk assessment. Subsequent design of additional food protein anaphylaxis algorithms is needed to improve the precision and efficiency of clinical food allergy assessment and immunotherapy treatment.

Clinical performance of a multimodal screening blood test for advanced adenomas and CRC in an average-risk cohort of 1,038 participants.

75 Background: The National Polyp Study and 2021 USPSTF CRC-update highlight that the detection and removal of precancerous advanced adenomas (AA) prevents colorectal cancer (CRC), decreases mortality, and leads to higher cost savings than early cancer detection. Performance of a multimodal blood-based test for the detection of CRC and AA that integrates sensitive and accurate detection of circulating gastrointestinal epithelial cells and somatic oncologic variants as well as SEER data of the impact of sex and age is described. Methods: The prospective study included average-risk, asymptomatic screening subjects from 18 geographically dispersed US colonoscopy centers with blood drawn before colonoscopy. Monte Carlo cross-validation (MCCV) methods were used to evaluate the robustness of test performance through 2000 iterations of independent training and validation using bootstrap resampling with stratification to balance patient histopathology, age, and gender. Results include point estimates, confidence intervals, and distributions of sensitivity and specificity to detect AA and CRC. Results: The study cohort (53.2% female; mean age 56.7 yrs.) consisted of 1,038 subjects (White 65.1%; Black 8.5%; Hispanic 24.8%; Asian 1.7%); of which 954 (92%) were asymptomatic, average-risk screening subjects without age enrichment (including 11 CRC and 93 AA) and 84 (8%) were enriched case-control (65 CRC and 19 AA) subjects. The algorithm derives a test score from 0 (low risk) to 100 (high risk) as a quantitative measure of AA and CRC risk. A pre-defined cut point of 47.2 yields a test specificity &gt; 90%, and 92.1% and 54.5% sensitivity for the detection of CRC and AA, respectively. Estimated sensitivities and selected Clopper-Pearson (Exact) 95% confidence intervals based on validation results from MCCV are presented. A split analysis shows for the 954 intended-use, asymptomatic, average-risk screening subjects, the sensitivity for CRC and AA are 100% and 55.9%. Conclusions: A multi-site, prospective, average-risk CRC screening study using a multimodal assay had high sensitivity and specificity for AA and CRC. The quantitative correlation of test scores with disease pathology indicates that the modes of the assay interrogate the primary underlying pathophysiology of disease. The results demonstrate the potential of this novel test to meet the clinically unmet need for a noninvasive strategy for CRC screening and prevention that detects CRC and AA. Clinical trial information: NCT05127096 . [Table: see text]

The Optimization of Emergency Logistics Based on Quantitative Risk Measurement

High-risk societies are uncertain, ambiguous and unpredictable. Risk fluctuations are prone to the intertwining and overlapping of various accident hazards and safety risks. China is prone to the interweaving and overlapping of various accident hazards and safety risks, which leads to an increase in the number of factors affecting public safety as well [1]. Therefore, this paper will quantify specific ranking indicators in terms of frequency of hazards, degree of hazards and vulnerability to different hazards. The ranking is based on the quantified results in red, yellow and green and the hazard risk level is calculated according to the risk formula. This study will take the Chengdu-Chongqing region as an example and optimise the storage of materials in the emergency logistics base in order to maximise the precise and rapid supply of materials to the main demand points. The efficiency of emergency material deployment will be further improved and emergency materials will be well secured.

Evaluating the likelihood of the glass cliff phenomenon for female ceos in college and universities

The glass cliff phenomenon (GCP) maintains that women are more likely to lead risky organizations than men. Quantitative measures of risk for assessing a possible GCP exist for leaders in the business sector, but no quantitative measures exist for college or university leadership. This article tests one variable of risk for college and university institutions, the financial responsibility composite score (FRCS), to see if it measures the GCP in college or university chief executive officer (CEO) hires. Using an ex post facto causal-comparative design, gender and FRCS in private American colleges and universities were analyzed using the Integrated Postsecondary Education Data System data. Purposive sampling randomly paired institutions with female CEOs with similar institutions with male CEOs. Using the U.S. Department of Education’s FRCS database, the researcher reviewed patterns in FRCS before and after CEO hires over three studies. No statistically significant results were observed, demonstrating that there is no indication that female CEOs are hired into riskier colleges or universities when utilizing FRCS. Future studies should further investigate the GCP using alternative measures of risk.

Bayesian Approach for Numerical Calculation of the Risk of Medical Evacuation by Vehicles

An original logical-probabilistic method for calculating the risk of critical patients’ medical evacuation by vehicles is proposed. A comprehensive estimation of road conditions and risk factors for transporting an intensive care patient as well as the calculation of a formal quantitative measure of transportation risk by the Bayesian inference method is considered. An effective instrument for estimating transportation risks for management decision making is developed.

RISK MANAGEMENT IN THE IMPLEMENTATION OF PROCUREMENT PROCEDURES FOR THE NUCLEAR POWER PLANTS CONSTRUCTION

In this paper, the authors consider approaches to risk management in the nuclear industry and carry out a risk analysis in the implementation of procurement procedures for the nuclear power plants construction. The problem under study is relevant on the scale of the construction of typical large industrial facilities, goods, works and services for which they are supplied through the public procurement system. This study is significant both in the practical field of application of existing methods for assessing project risks, and in the theoretical field of studying various combinations of methods for qualitative and quantitative risk measurement. The risk analysis was carried out on the basis of data on equipment purchases made during the construction of the Leningrad NPP-2. The authors made a forecast of the delay from the date of delivery of equipment under the contract. To develop recommendations on the formation of a temporary reserve for the supply of equipment, three concluded contracts for the supply of equipment for the construction of power units of the Akkuyu NPP (Turkey) were selected. As a result of the study, recommendations were formulated for risk management of the NPP construction project implemented through the public procurement system.

Associations of Air Pollution and Genetic Risk With Incident Dementia: A Prospective Cohort Study.

Evidence on the association between air pollution and dementia is accumulating but still inconclusive, and the potential effect modification by genetics is unclear. We investigated the joint effects of air pollution exposure and genetic risk on incident dementia in a prospective cohort study, the UK Biobank study. Land use regression models were used to estimate exposure to ambient particulate matter (PM) in 3 fraction sizes (PM with diameter < 2.5 μm (PM2.5), coarse particles (PM with diameter 2.5-10 μm (PMc)), and PM with diameter <10 μm (PM10)), PM2.5 absorbance, nitrogen dioxide levels, and nitrogen oxide levels at each individual's baseline residence. A polygenic risk score was calculated as a quantitative measure of genetic dementia risk. Incident cases of dementia were ascertained through linkage to health administrative data sets. Among the 227,840 participants included in the analysis, 3,774 incident dementia cases (including 1,238 cases of Alzheimer disease and 563 cases of vascular dementia) were identified. After adjustment for a variety of covariates, including genetic factors, positive associations were found between exposure to air pollution-particularly PM10, PM2.5 absorbance, and nitrogen dioxide-and incident all-cause dementia and Alzheimer disease but not vascular dementia. No significant interaction between air pollution and genetics was found, either on the multiplicative scale or on the additive scale. Exposure to air pollution was associated with a higher risk of developing dementia regardless of genetic risk.

Predicting Suicide in Counties: Creating a Quantitative Measure of Suicide Risk.

Rising rates of suicide over the past two decades have increased the need for wide-ranging suicide prevention efforts. One approach is to target high-risk groups, which requires the identification of the characteristics of these population sub-groups. This suicidology study was conducted using large-scale, secondary data to answer the question: using the research on suicide, are there variables studied at the community level that are linked to suicide and are measurable using quantitative, demographic data that are already collected and updated? Data on deaths from suicide in U.S. counties for the years 2000, 2005, 2010 and 2015 were analyzed using multiple regression, longitudinal regression, and cluster analysis. Results indicated that the suicide rate in a county can be predicted by measuring the financial stability of the residents, the quality of mental health in the county, and the economic opportunity in the county. The results are further analyzed using two sociological theories, Social Strain Theory and the Theory of Anomie, and two psychological theories, the Shame Model and the Interpersonal Theory of Suicide.

Bank Rating Gaps as Proxies for Systemic Risk

Banks receive two types of ratings from major rating agencies: an “all-in” and a “stand-alone” rating. This paper investigates whether rating gaps between all-in ratings and stand-alone ratings could serve as a useful measure for the systemic risk of banks. Using US data from 1994 to 2007, the link between the rating gaps and a quantitative systemic risk measure, Co-independent Value at Risk (CoVar), is examined. The conclusion is that rating gaps are good proxies for systemic risk of large banks.

Prognosis of Epileptic Seizure Event Onsets Using Random Survival Forests

This article introduces a machine learning approach, based on a nonparametric, decision-tree-based random survival forest (RSF) model, for a continuous prognosis of epileptic seizure events using electroencephalogram (EEG) data. While earlier seizure prediction methods forecast seizure occurrences only at a specified future time, the RSF model allows estimation of the probability of seizure onset, in terms of a hazard function, over the entire prediction horizon. These estimates are crucial for developing individualized quantitative risk measures and management plans for epilepsy patients. Additionally, RSF can identify the key risk factors by capturing the interdependencies among the features extracted from EEG data. The performance of RSF was evaluated for prognosing seizure onsets of the rat and mice specimens in an 80 small animals cohort at the Texas A&M Department of Neuroscience and Experimental Therapeutics. The results suggest that RSF outperforms other contemporary survival models, including the popular Cox proportional hazard, with 87.5% lower integrated Brier Score (IBS) errors, and 17.5% higher concordance index (C-index). Further, a continuous seizure prediction sensitivity of 83% and specificity of 87% were obtained even over a 5-min prediction horizon (the average time between successive seizure onsets was 5 min long). These results suggest that the RSF model can be used to effectively quantify the likelihood of seizure onsets over time to the patients and caregivers, promoting informed decision making.

Impact of risk disclosures on IPO performance: Evidence from India

The main aim of the paper is to explore the performance of Indian IPOs in the context of risk disclosures in the offer documents. For the purpose of assessing the impact of risk disclosure factors on initial returns, subsequent returns and post issue risk of IPOs, the study has implemented ordinary least square regression. The study has analysed 109 IPOs that were listed in two main Indian stock exchanges (BSE and NSE) from 2015–2019. Outcomes of the present study are contrary to the previous studies which showed that information disclosure reduces the asymmetry, which is touted as the main reason for underpricing, the present study did not find any association between risk disclosures and underpricing. Quantitative risk measures showed positive association with 1-year returns, but qualitative measures failed to show any association. The post issue risk of the firms showed positive association with external risk factors listed in prospectus and negative association with liquidity. The results of this study are useful for the investors as based on the results they can make decisions about investing in Indian IPOs. Besides, the managers of issuing companies and lead managers of issues can use the results of this study to improve the pricing of issues. To the best of the authors’ knowledge no study has been done before in the Indian context which is specific to risk disclosures (quantitative and qualitative measures) and IPO performance. The present study seeks to fill this gap and contribute to the existing literature.

A sentiment-based risk indicator for the Mexican financial sector

We apply sentiment analysis to Twitter messages in Spanish to build a sentiment risk index for the financial sector in Mexico. We classify a sample of tweets from 2006-2019 to identify messages in response to a positive or negative shock to the Mexican financial sector, relative to merely informative ones. We use a voting classifier approach based on three different classifiers: one based on word polarities from a pre-defined dictionary, one based on a support vector machine classifier, and one based on neural networks. We find that the voting classifier outperforms each of the other classifiers when taken alone. Next, we compare our sentiment index with existing indicators of financial stress based on quantitative variables. We find that this novel index captures the impact of sources of financial stress not explicitly encompassed in quantitative risk measures, such as financial frauds, failures in payment systems, and money laundering. Finally, we show that a shock in our Twitter sentiment index correlates positively with an increase in financial market risk, stock market volatility, sovereign risk, and foreign exchange rate volatility.

A shared decision-making communications workshop improves internal medicine resident skill, risk-benefit education, and counseling attitude

ObjectiveWe assessed the impact of a workshop on first-year medicine residents (PGY1) shared decision-making (SDM) communication skill, risk-benefit education, and attitude. MethodsA SDM skills-focused workshop was integrated into an academic medical center PGY1 ambulatory rotation in 2016–2017. Pre/post recordings of virtual Objective Structured Clinical Examinations (OSCEs) with standardized patients sharing decisions were scored using OPTION5. Risk-benefit education, including decision aid use, was measured. Pre/post surveys assessed SDM practice attitudes and perceived barriers. Results31 of 48 (65%) PGY1 workshop attendees completed pre/post OSCEs yielding 62 videos. OPTION5 scores improved from 27/100 pre to 56/100 post (p < 0.001). Pre/post increases in integration of qualitative (15/31 vs 31/31, p < 0.001) and quantitative (3/31 vs 31/31, p < 0.001) risk measures, and decision aids (1/3 vs 31/31, p < 0.001) were observed. Pro-SDM attitude of decisional neutrality increased 16.6% pre to 71.9% post-survey (P < 0.001). Barriers to SDM remain. ConclusionThis PGY1 workshop with virtual OSCEs improved SDM communication skills, the ability to find and provide risk-benefit education, and SDM-facilitating attitude. Practice implicationsResidency programs can improve SDM skills, risk-benefit education, and attitudes with a workshop intervention. Perceived time constraints and cognitive biases regarding risk-benefit estimates should be addressed to ensure quality SDM in practice.

A Methodology for Harmonizing Safety and Health Scales in Occupational Risk Assessment.

Successful implementation of Health and Safety (H&S) systems requires an effective mechanism to assess risk. Existing methods focus primarily on measuring the safety aspect; the risk of an accident is determined based on the product of severity of consequence and likelihood of the incident arising. The health component, i.e., chronic harm, is more difficult to assess. Partially, this is due to both consequences and the likelihood of health issues, which may be indeterminate. There is a need to develop a quantitative risk measurement for H&S risk management and with better representation for chronic health issues. The present paper has approached this from a different direction, by adopting a public health perspective of quality of life. We have then changed the risk assessment process to accommodate this. This was then applied to a case study. The case study showed that merely including the chronic harm scales appeared to be sufficient to elicit a more detailed consideration of hazards for chronic harm. This suggests that people are not insensitive to chronic harm hazards, but benefit from having a framework in which to communicate them. A method has been devised to harmonize safety and harm risk assessments. The result was a comprehensive risk assessment method with consideration of safety accidents and chronic health issues. This has the potential to benefit industry by making chronic harm more visible and hence more preventable.