Forensic DNA Trace Evidence Interpretation: Activity Level Propositions and Likelihood Ratios provides all foundational information required for a reader to understand the practice of evaluating forensic biology evidence given activity level propositions and to implement the practice into active casework within a forensic institution. The book begins by explaining basic concepts and foundational theory, pulling together research and studies that have accumulated in forensic journal literature over the last 20 years.The book explains the laws of probability - showing how they can be used to derive, from first principles, the likelihood ratio - used throughout the book to express the strength of evidence for any evaluation. Concepts such as the hierarchy of propositions, the difference between experts working in an investigative or evaluative mode and the practice of case assessment and interpretation are explained to provide the reader with a broad grounding in the topics that are important to understanding evaluation of evidence. Activity level evaluations are discussed in relation to biological material transferred from one object to another, the ability for biological material to persist on an item for a period of time or through an event, the ability to recover the biological material from the object when sampled for forensic testing and the expectations of the prevalence of biological material on objects in our environment. These concepts of transfer, persistence, prevalence and recovery are discussed in detail in addition to the factors that affect each of them.The authors go on to explain the evaluation process: how to structure case information and formulate propositions. This includes how a likelihood ratio formula can be derived to evaluate the forensic findings, introducing Bayesian networks and explaining what they represent and how they can be used in evaluations and showing how evaluation can be tested for robustness. Using these tools, the authors also demonstrate the ways that the methods used in activity level evaluations are applied to questions about body fluids. There are also chapters dedicated to reporting of results and implementation of activity level evaluation in a working forensic laboratory. Throughout the book, four cases are used as examples to demonstrate how to relate the theory to practice and detail how laboratories can integrate and implement activity level evaluation into their active casework.
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Introduction Coastal locations contribute significantly to global drowning, with surfers frequently conducting rescues. This study explored the characteristics of surfers as bystander rescuers in Europe. Methods A cross-sectional online survey collected demographics (age, sex, geographical location), surfing experience, ability, lifesaving and cardiopulmonary resuscitation (CPR) training, information seeking behaviors and previous performance of a rescue. Analyses comprised descriptive frequencies, binomial logistic regression with adjusted odds ratio (AOR) (95% confidence interval [CI]) and chi-squares (p < .05). Results Europe-dwelling respondents totaled 1705 (76% male; 43% 25–34 years). Thirty-nine percent (39.2%; n = 668) had previously performed a rescue. Likelihood of having conducted a rescue significantly increased with 6 or more years of surfing experience (6–10 years [AOR = 1.96; 95%CI: 1.20–3.22]; 11–15 years [AOR = 3.26; 95%CI: 1.56–6.79]; 16 years or more [AOR = 4.27; 95%CI: 2.00–9.11]) when compared to surfers with <1 year experience. Expert/professional ability surfers were 10.89 times (95%CI: 4.72–25.15) more likely to have conducted a rescue than novice/beginners. Respondents who had received both a certified lifeguard and CPR course were significantly more likely to have conducted a rescue (AOR = 3.34; 95%CI: 2.43–4.60). Conclusion Surfers who had previously conducted rescues commonly had more years of experience, higher self-rated surf ability and greater likelihood of having received certified training. However, not all surfers who have performed rescues had received training. Findings suggest surfers should receive rescue and CPR training before they start surfing at locations without trained supervision and refresh training regularly. Surfers are amenable to injury prevention information, especially online and via apps.
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The study of human factors in forensic science informs our understanding of the interaction between humans and the systems they use. The Expert Working Group (EWG) on Human Factors in Forensic DNA Interpretation used a systems approach to conduct a scientific assessment of the effects of human factors on forensic DNA interpretation with the goal of recommending approaches to improve practice and reduce the likelihood and consequence of errors. This effort resulted in 44 recommendations. The EWG designed many of these recommendations to improve the production, interpretation, evaluation, documentation, and communication of DNA comparison results.
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Are professionals better at assessing the evidential strength of different types of forensic conclusions compared to students? In an online questionnaire 96 crime investigation and law students, and 269 crime investigation and legal professionals assessed three fingerprint examination reports. All reports were similar, except for the conclusion part which was stated in a categorical (CAT), verbal likelihood ratio (VLR) or numerical likelihood ratio (NLR) conclusion with high or low evidential strength. The results showed no significant difference between the groups of students and professionals in their assessment of the conclusions. They all overestimated the strength of the strong CAT conclusion compared to the other conclusion types and underestimated the strength of the weak CAT conclusion. Their background (legal vs. crime investigation) did have a significant effect on their understanding. Whereas the legal professionals performed better compared to the crime investigators, the legal students performed worse compared to crime investigation students.
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Digital forensic scientists primarily rely on individual internal reasoning and categorical conclusions when evaluating evidence in casework. This can make it difficult to maintain structured reasoning that is logically sound, balanced, robust, and transparent. Trojan horse defense cases exemplify these challenges in evaluating digital forensic findings. The key challenge in such cases is combining multiple observations into a logically sound probabilistic evaluation while maintaining an understandable forensic report for court and other recipients. To address these challenges, we propose using the likelihood ratio framework to evaluate digital findings in Trojan horse defense cases, with Bayesian networks serving to visualize the evaluation and derive a likelihood ratio. We will illustrate this approach by demonstrating the construction of a Bayesian network through a case example. We show that these networks are very suitable to model the evaluation of digital evidence in Trojan horse defense cases and that they can be easily adapted for various case circumstances. Based on our findings, we strongly recommend broader exploration of Bayesian networks in digital forensic casework.
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Background: The aim of this study is to validate a newly developed nurses' self-efficacy sources inventory. We test the validity of a five-dimensional model of sources of self-efficacy, which we contrast with the traditional four-dimensional model based on Bandura's theoretical concepts. Methods: Confirmatory factor analysis was used in the development of the newly developed self-efficacy measure. Model fit was evaluated based upon commonly recommended goodness-of-fit indices, including the χ2 of the model fit, the Root Mean Square Error of approximation (RMSEA), the Tucker-Lewis Index (TLI), the Standardized Root Mean Square Residual (SRMR), and the Bayesian Information Criterion (BIC). Results: All 22 items of the newly developed five-factor sources of self-efficacy have high factor loadings (range .40-.80). Structural equation modeling showed that a five-factor model is favoured over the four-factor model. Conclusions and implications: Results of this study show that differentiation of the vicarious experience source into a peer- and expert based source reflects better how nursing students develop self-efficacy beliefs. This has implications for clinical learning environments: a better and differentiated use of self-efficacy sources can stimulate the professional development of nursing students.
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BACKGROUND: Ankle decision rules are developed to expedite patient care and reduce the number of radiographs of the ankle and foot. Currently, only three systematic reviews have been conducted on the accuracy of the Ottawa Ankle and Foot Rules (OAFR) in adults and children. However, no systematic review has been performed to determine the most accurate ankle decision rule.OBJECTIVES: The purpose of this study is to examine which clinical decision rules are the most accurate for excluding ankle fracture after acute ankle trauma.METHODS: A systematic search was conducted in the databases PubMed, CINAHL, PEDro, ScienceDirect, and EMBASE. The sensitivity, specificity, likelihood ratios, and diagnostic odds ratio of the included studies were calculated. A meta-analysis was conducted if the accuracy of a decision rule was available from at least three different experimental studies.RESULTS: Eighteen studies satisfied the inclusion criteria. These included six ankle decision rules, specifically, the Ottawa Ankle Rules, Tuning Fork Test, Low Risk Ankle Rule, Malleolar and Midfoot Zone Algorithms, and the Bernese Ankle Rules. Meta-analysis of the Ottawa Ankle Rules (OAR), OAFR, Bernese Ankle Rules, and the Malleolar Zone Algorithm resulted in a negative likelihood ratio of 0.12, 0.14, 0.39, and 0.23, respectively.CONCLUSION: The OAR and OAFR are the most accurate decision rules for excluding fractures in the event of an acute ankle injury.
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Recent research has indicated an increase in the likelihood and impact of tree failure. The potential for trees to fail relates to various biomechanical and physical factors. Strikingly, there seems to be an absence of tree risk assessment methods supported by observations, despite an increasing availability of variables and parameters measured by scientists, arborists and practitioners. Current urban tree risk assessments vary due to differences in experience, training, and personal opinions of assessors. This stresses the need for a more objective method to assess the hazardousness of urban trees. The aim of this study is to provide an overview of factors that influence tree failure including stem failure, root failure and branch failure. A systematic literature review according to the PRISMA guidelines has been performed in databases, supported by backward referencing: 161 articles were reviewed revealing 142 different factors which influenced tree failure. A meta-analysis of effect sizes and p-values was executed on those factors which were associated directly with any type of tree failure. Bayes Factor was calculated to assess the likelihood that the selected factors appear in case of tree failure. Publication bias was analysed visually by funnel plots and results by regression tests. The results provide evidence that the factors Height and Stem weight positively relate to stem failure, followed by Age, DBH, DBH squared times H, and Cubed DBH (DBH3) and Tree weight. Stem weight and Tree weight were found to relate positively to root failure. For branch failure no relating factors were found. We recommend that arborists collect further data on these factors. From this review it can further be concluded that there is no commonly shared understanding, model or function available that considers all factors which can explain the different types of tree failure. This complicates risk estimations that include the failure potential of urban trees.
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Objective The evaluation of voice quality is a major component of voice assessment. The aim of the present study was to develop a new multivariate acoustic model for the evaluation of breathiness. Method Concatenated voice samples of continuous speech and the sustained vowel [a:] from 970 subjects with dysphonia and 88 vocally healthy subjects were perceptually judged for breathiness severity. Acoustic analyses were conducted on the same concatenated voice samples after removal of the non-voiced segments of the continuous speech sample. The development of an acoustic model for breathiness was based on stepwise multiple linear regression analysis. Concurrent validity, diagnostic accuracy, and cross validation were statistically verified on the basis of the Spearman rank-order correlation coefficient (rs), several estimates of the receiver operating characteristics plus the likelihood ratio, and iterated internal cross correlations. Results Ratings of breathiness from four experts with moderate reliability were used. Stepwise multiple regression analysis yielded a nine-variable acoustic model for the multiparametric measurement of breathiness (Acoustic Breathiness Index [ABI]). A strong correlation was found between ABI and auditory-perceptual rating (rs = 0.840, P = 0.000). The cross correlations confirmed a comparably high degree of association. Additionally, the receiver operating characteristics and likelihood ratio results showed the best diagnostic outcome at a threshold of ABI = 3.44 with a sensitivity of 82.4% and a specificity of 92.9%. Conclusions This study developed a new acoustic multivariate correlate for the evaluation of breathiness in voice. The ABI model showed valid and robust results and is therefore proposed as a new acoustic index for the evaluation of breathiness.
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Objective In voice assessment, the evaluation of voice quality is a major component in which roughness has received wide acceptance as a major subtype of abnormal voice quality. The aim of the present study was to develop a new multivariate acoustic model for the evaluation of roughness. Method In total, 970 participants with dysphonia and 88 participants with normal voice were included. Concatenated voice samples of continuous speech and sustained vowel [a:] were perceptually judged on roughness severity. Acoustic analyses were conducted on the voiced segments of the continuous speech sample plus sustained vowel as well. A stepwise multiple linear regression analysis was applied to construct an acoustic model of the best acoustic predictors. Concurrent validity, diagnostic accuracy, and cross-validation were verified on the basis of Spearman correlation coefficient (rs), several estimates of the receiver operating characteristics plus the likelihood ratio, and iterated internal cross-correlations. Results Six experts were included for perceptual analysis based on acceptable rater reliability. Stepwise multiple regression analysis yielded a 12-variable acoustic model. A marked correlation was identified between the model and the perceptual judgment (rs = 0.731, P = 0.000). The cross-correlations confirmed a high comparable degree of association. However, the receiver operating characteristics and likelihood ratio results showed the best diagnostic outcome at a threshold of 2.92, with a sensitivity of 51.9% and a specificity of 94.9%. Conclusions Currently, the newly developed roughness model is not recommended for clinical practice. Further research is needed to detect the acoustic complexity of roughness (eg, multiplophonia, irregularity, chaotic structure, glottal fry, etc).
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