State of damage to and support for victims of motor vehicle accidents in Japan
وضعیت آسیب و پشتیبانی قربانیان حوادث وسایل نقلیه موتوری در ژاپن-2019
Individuals are likely to be involved in at least one motor vehicle accident (MVA) during their lifetime.MVAs can have a significant impact on both the victimsand their families; in the case of death, the bereaved familymay face mental health problems. Ongoing studies have focused on devising strategies to support victims and their families who face such problems. This paper clarifies the reality of mental health issues ofMVA victims and reviews the current state of victimsupport available in Japan, its significance and other relevant issues. The prevalence of post-traumatic stress disorder (PTSD) inMVA survivors has been estimated to be 8%–45% one month after the accident and 6%–40% six months after the accident. The mental health of the survivors families, bereaved families, and orphaned children are usually affected afterMVAs. Bereaved families experience not only PTSD but also symptoms of complicated grief. Based on studies using different scales to measure symptoms and other items, symptoms of PTSD and complicated grief have been seen in 17%–75% and 6%–61% of bereaved families, respectively, which were much higher than those observed in the general population. In addition to the actual physical andmental damage caused byMVAs, it is necessary to take notice of survivors who are exposed to post-accident secondary victimization. Justice agencies, such as the National Police Agency andMinistry of Justice Investigation Bureau, as well as victim support centers and self-help groups, provide support to MVA victims. To a certain extent, evaluating support provided to MVA victims and their families is possible by initiating assistance promptly and actively using leaflets, brochures, and other materials. The literature reports thatwomen are at increased risk for developing PTSD and complicated grief; also,men and women use differentmechanisms for coping with stress.Moreover, men tend not to express their pain and try to manage it on their own. Thus, support that is appropriate for both sexes must be provided. In the future, the effectiveness of the support provided should be evaluated by survivors. Whether acute-phase support leads to improvement in survivors long-term prognoses must also be investigated.
Keywords: Motor vehicle accidents | MVA victims | Bereaved family | Social support | Self-help groups | Sex differences
Gendered drug policy: Motherisk and the regulation of mothering in Canada
سیاست مواد مخدر جنسیتی: Motherisk و تنظیم مادرانگی در کانادا-2019
Background: Due to misinformation and enduring discourses about pregnant women and mothers suspected of using drugs, these women continue to experience systemic discrimination. In 2014, this fact was once again made public in Canada when the Ontario government established an independent review of hair testing practices conducted by Motherisk Drug Testing Laboratory (MDTL) at Torontos Hospital for Sick Kids. Between 2005 and 2015, MDTL tested the hair of more than 16,000 individuals for drug consumption. The results were introduced as evidence in court and resulted in both temporary and permanent loss of custody of children. Tragically, it was later discovered that the hair testing results were unreliable. This paper provides an analysis of child protection policies and practices directed at pregnant women and mothers suspected of using drugs, with a focus on the Motherisk tragedy in Ontario. Methods: Informed by feminist and critical drug perspectives, this study draws from findings in the 2015″Report of the Motherisk Hair Analysis Independent Review," produced by Honourable Susan Lang, and provides a Bacchi-informed critical analysis of Commissioner Beamans 2018 report of the Motherisk Commission, "Harmful Impacts: The Reliance on Hair Testing in Child Protection" (HI). Results: The HI report is quite sympathetic to the plight of families and it acknowledges systemic issues and unequal power relations between families, social workers and the courts. Even though drug testing is an inadequate measure of parenting capacity, the HI report does not recommend banning the practice. In the HI report, the themes of harm reduction and drug prohibition are notably absent — while the use of gender-neutral terms, such as "parent" and "families," render mothers invisible. Conclusions: The Motherisk tragedy cannot be understood as an isolated event, rather it is part of a continuum of state and gendered violence against poor, Indigenous, and Black women in Canada. The HI report fails to consider how prohibitionist discourses about drugs, addiction, mothering, and risk lead to institutional practices such as drug testing and child apprehension.
Keywords: Motherisk | Women | Race | Drug testing | Child apprehension
Automated segmentation of cell membranes to evaluate HER2 status in whole slide images using a modified deep learning network
تقسیم بندی خودکار غشای سلولی برای ارزیابی وضعیت HER2 در تصاویر اسلاید کل با استفاده از یک شبکه یادگیری عمیق اصلاح شده-2019
The uncontrollable growth of cells in the breast tissue causes breast cancer which is the second most common type of cancer affecting women in the United States. Normally, human epidermal growth factor receptor 2 (HER2) proteins are responsible for the division and growth of healthy breast cells. HER2 status is currently assessed using immunohistochemistry (IHC) as well as in situ hybridization (ISH) in equivocal cases. Manual HER2 evaluation of IHC stained microscopic images involves an error-prone, tedious, inter-observer variable, and time-consuming routine lab work due to diverse staining, overlapped regions, and non-homogeneous remarkable large slides. To address these issues, digital pathology offers reproducible, automatic, and objective analysis and interpretation of whole slide image (WSI). In this paper, we present a machine learning (ML) framework to segment, classify, and quantify IHC breast cancer images in an effective way. The proposed method consists of two major classifying and segmentation parts. Since HER2 is associated with tumors of an epithelial region and most of the breast tumors originate in epithelial tissue, it is crucial to develop an approach to segment different tissue structures. The proposed technique is comprised of three steps. In the first step, a superpixel-based support vector machine (SVM) feature learning classifier is proposed to classify epithelial and stromal regions from WSI. In the second stage, on classified epithelial regions, a convolutional neural network (CNN) based segmentation method is applied to segment membrane regions. Finally, divided tiles are merged and the overall score of each slide is evaluated. Experimental results for 127 slides are presented and compared with state-of-the-art handcraft and deep learning-based approaches. The experiments demonstrate that the proposed method achieved promising performance on IHC stained data. The presented automated algorithm was shown to outperform other approaches in terms of superpixel based classifying of epithelial regions and segmentation of membrane staining using CNN.
Keywords: CNN | Digital pathology | Whole slide image | Deep learning | HER2 assessment | Membrane segmentation
Medico-legal considerations and operative vaginal delivery
ملاحظات پزشکی قانونی و زایمان واژینال عملیاتی-2019
Women undergo operative vaginal delivery (OVD) as an alternative to caesarean section when complications arise in the second stage of labour. The perinatal mortality associated with OVD is very low, and most of the perinatal morbidity is minor. However, when serious adverse events occur, such as traumatic birth injury, shoulder dystocia, cerebral palsy and perinatal death, there are medico-legal implications. There is also the potential for litigation in relation to maternal pelvic floor injury, which is increased with OVD. Obstetricians performing and supervising OVDs need to be aware of the potential pitfalls and minimise the risk of adverse outcomes. Given that most obstetricians will be involved in adverse birth-related events, it is important that they are aware of the legal processes that may ensue. It is also important when reviewing adverse OVD-related outcomes that association is differentiated from causation. These issues are addressed in the current chapter with attention drawn to the Montgomery ruling, which redefines the legal standards expected in relation to informed consent.
Keywords: Operative vaginal delivery (OVD) | Serious adverse events | Litigation | Medico-legal | Causation | Montgomery ruling
The Irish Journey: Removing the shackles of abortion restrictions in Ireland
سفر ایرلند: برداشتن موارد محدودیت سقط جنین در ایرلند-2019
In May 2018, the Irish electorate voted to remove from the Constitution one of the most restrictive abortion bans in the world. This referendum followed 35 years of legal cases, human rights advocacy, feminist activism and governmental and parliamentary processes. The reframing of abortion as an issue of womens health rather than foetal rights was crucial to the success of law reform efforts. The new law, enacted in 2018, provides for access to abortion on a womans request up to 12 weeks of pregnancy and in situations of risk to the life or of serious harm to the health of the pregnant woman and fatal foetal anomaly thereafter. Abortion is now broadly accessible in Ireland; however, continued advocacy is needed to ensure that the state meets international human rights standards and that access to abortion care and abortion rights is fully secured into the law.
Keywords: Abortion | Ireland | Human rights | Advocacy | Legalisation
CWV-BANN-SVM ensemble learning classifier for an accurate diagnosis of breast cancer
طبقه بندی یادگیری گروه CWV-BANN-SVM برای تشخیص دقیق سرطان پستان-2019
This paper presents a new data mining technique for an accurate prediction of breast cancer (BC), which is one of the major mortality causes among women around the globe. The main objective of our study is to expand an automatic expert system (ES) to provide an accurate diagnosis of BC. Both, Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) were applied to analyze BC data. The wellknown Wisconsin Breast Cancer Dataset (WBCD), available in the UCI repository, was examined in our study. We first tested the SVM algorithm using various values of the C, e and c parameters. As a result of the first experiment, we were able to observe that the adjustment of these regularization parameters can greatly improve the performance of the traditional SVM algorithm applied for BC detection. The highest obtained accuracy at the first step was 99.71%. Then, we performed a new BC detection approach based on two ensemble learning techniques: the confidence-weighted voting method and the boosting ensemble technique. Our model, called CWV-BANNSVM, combines boosting ANNs (BANN) and two SVMs, using optimal parameters selected during the first experiment. The performance of the applied methods was evaluated using several popular metrics, such as specificity, sensitivity, precision, FPR, FNR, F1 score, AUC, Gini and accuracy. The proposed CWV-BANNSVM model was able to improve the performance of the traditional machine learning algorithms applied to BC detection, reaching the accuracy of 100%. To overcome the overfitting issue, we determined and used some appropriate parameter values of polynomial SVM. Our comparison with the existing studies dedicated to BC prediction suggests that the proposed CWV-BANN-SVM model provides one of the best prediction performances overall.
Keywords: Data mining | Machine learning | Ensemble technique | Breast cancer | Support vector machine | Artificial neural network
The other side of the same coin – How communal beliefs about entrepreneurship influence attitudes toward entrepreneurship
طرف دیگر همان سکه - چگونه باورهای جمعی درباره کارآفرینی بر نگرش به کارآفرینی تأثیر می گذارد-2019
Drawing on the information processing perspective, this paper investigates how young adults attitude toward entrepreneurship is shaped by their beliefs about the role and activities of entrepreneurs. Our first study (N=129) reveals that young adults hold a biased set of beliefs. They believe that entrepreneurship affords agentic aspects (e.g., achievement, power, excitement), but significantly less believe in communal aspects which are, however, equally integral to entrepreneurship (e.g., interaction, pro-social behavior). In a subsequent experimental vignette study (N=389), we show, that communicating the communal nature of entrepreneurship, specifically the pro-social aspects, improves both mens and womens attitude toward entrepreneurship. Overall, our research suggests that portrayals of occupations shape young adults beliefs about career options and thereby influence their attitude toward respective careers.
Keywords: Occupational portrayal | Belief | Attitude | Communion | Experimental study | Information processing perspective
“To boldly go where no [man] has gone before” - Institutional voids and the development of womens digital entrepreneurship
"شجاعانه به آنجا بروید که هیچ کس قبل از آن نرفته باشد" - خلاء نهادی و توسعه کارآفرینی دیجیتالی زنان-2019
This paper examines the emergence of digital entrepreneurship in the context of emerging economies. Given that these economies generally lack a well-developed institutional framework, we draw on the concept of institutional voids as our theoretical lens. We argue that digital entrepreneurship facilitates the navigation and bridging of socio-cultural institutional voids but also provides opportunities for entrepreneurs to directly and indirectly alter the existing institutional context. We illustrate these arguments by drawing upon six biographical narrations of female digital entrepreneurs in Saudi Arabia. Accordingly, through our development of a multi-level model, we make explicit the two-way causal interaction between entrepreneurial action, institution altering behaviour and the social and cultural context, thus providing a framework for future research
Keywords: Saudi Arabia | Institutional voids | Digital entrepreneurship | Female entrepreneurs | Biographical narrations
Gender gap in entrepreneurship
شکاف جنسیتی در کارآفرینی-2019
Using data on the entire population of businesses registered in the states of California and Massachusetts between 1995 and 2011, we decompose the well-established gender gap in entrepreneurship. We show that femaleled ventures are 63 percentage points less likely than male-led ventures to obtain external funding (i.e., venture capital). The most significant portion of the gap (65 percent) stems from gender differences in initial startup orientation, with women being less likely to found ventures that signal growth potential to external investors. However, the residual gap is as much as 35 percent and much of this disparity likely reflects investors’ gendered preferences. Consistent with theories of statistical discrimination, the residual gap diminishes significantly when stronger signals of growth are available to investors for comparable female- and male-led ventures or when focal investors appear to be more sophisticated. Finally, conditional on the reception of external funds (i.e., venture capital), women and men are equally likely to achieve exit outcomes, through IPOs or acquisitions.
Keywords: Entrepreneurship | Gender | Venture capital | Discrimination
Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry
تفاوت های جنسیتی در عملکرد تشخیصی یادگیری دستگاه یادگیری عروق کرونر CT-نتیجه حاصل از کسری جریان کسری ناشی از آنژیوگرافی از رجیستری ماشین-2019
Purpose: This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFRML) for the detection of lesion-specific ischemia. Method: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR≤0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis ≥50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis. Results: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72–84), 79% (95%CI 73–84), 75% (95%CI 69–79), and 82% (95%CI: 76–86) in men vs. 75% (95%CI 58–88), 81 (95%CI 72–89), 61% (95%CI 50–72) and 89% (95%CI 82–94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79–0.87] vs. 0.83 [95%CI 0.75–0.89], p=0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75–0.89) vs. 0.74 (95%CI: 0.65–0.81), p=0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79–0.87) vs. 0.76 (95%CI: 0.71–0.80), p=0.007]. Conclusions: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.
Keywords: Coronary artery disease | Machine learning | Spiral computed tomography | Fractional flow reserve