Trajectories of drug use among French young people : prototypical stages of involvement in illicit drug use

Aims: This study investigated patterns and trajectories of substance use, with a special focus on illicit drugs other than cannabis. It examined both patterns and trajectories of use among a general population-based sample. Methods: We used data from the 2011 French ESCAPAD survey of French 17-year-olds to assess exposure and age of initiation of 14 licit and illicit drugs (N=23,882). Latent class analysis (LCA) and survival analyses were performed. Results: The results of the LCA showed that patterns of illicit drug use clearly distinguished between two groups of other illicit drugs: 1) amphetamines/speed, cocaine, ecstasy/MDMA, magic mushrooms, poppers, and solvents; 2) crack/freebase, GHB/GBL, heroine, LSD, and ketamine. Survival analyses highlighted that trajectories involved the first group before the second one. Conclusions: Prototypical drug use patterns and trajectories should include a distinction between two groups of illicit drugs. Preventive actions should focus on young people in their early teens, since very young users are more likely to progress to illicit drug use, and further studies should include this distinction instead of aggregating other illicit drugs into a single category

Hidden walls: STEM course barriers identified by students with disabilities

Historically, non-disabled individuals have viewed disability as a personal deficit requiring change to the disabled individual. However, models have emerged from disability activists and disabled intellectuals that emphasize the role of disabling social structures in preventing or hindering equal access across the ability continuum. We used the social relational proposition, which situates disability within the interaction of impairments and particular social structures, to identify disabling structures in introductory STEM courses. We conducted interviews with nine students who identified with a range of impairments about their experiences in introductory STEM courses. We assembled a diverse research team and analyzed the interviews through phenomenological analysis. Participants reported course barriers that prevented effective engagement with course content. These barriers resulted in challenges with time management as well as feelings of stress and anxiety. We discuss recommendations for supporting students to more effectively engage with introductory STEM courses

Detection of Inferior Myocardial Infarction: A Comparison of Various Decision Systems and Learning Algorithms

Abstract In this work we have focused on classification of inferior myocardial infarction (MI)

Religion and Self: Notions from a Cultural Psychological Perspective

After a brief introduction of a cultural psychological perspective, this paper turns to the concept of self. The paper proposes to conceive of that reality to which the concepts of self refer as a narrative, employing especially autobiographies and other ego-documents in empirical exploration. After discussing some psychological theories about “self,” the paper points out that they may well be applied in research on personal religiosity

Classification of Foetal Distress and Hypoxia Using Machine Learning Approaches

© 2018, Springer International Publishing AG, part of Springer Nature. Foetal distress and hypoxia (oxygen deprivation) is considered as a serious condition and one of the main factors for caesarean section in the obstetrics and Gynecology department. It is the third most common cause of death in new-born babies. Many foetuses that experienced some sort of hypoxic effects can develop series risks including damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe the foetal well being. Foetal surveillance by monitoring the foetal heart rate with a cardiotocography is widely used. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. In this paper, machine-learning algorithms are utilized to classify foetuses which are experiencing oxygen deprivation using PH value (a measure of hydrogen ion concentration of blood used to specify the acidity or alkalinity) and Base Deficit of extra cellular fluid level (a measure of the total concentration of blood buffer base that indicates the metabolic acidosis or compensated respiratory alkalosis) as indicators of respiratory and metabolic acidosis, respectively, using open source partum clinical data obtained from Physionet. Six well know machine learning classifier models are utilised in our experiments for the evaluation; each model was presented with a set of selected features derived from the clinical data. Classifier’s evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as the confusion matrix. Our simulation results indicate that machine-learning algorithms provide viable methods that could delivery improvements over conventional analysis

Writing Toward Readers\u27 Better Health: A Case Study Examining the Development of Online Health Information

Each year, more people search the Internet for health information. Through a case study conducted at a prominent health information company, I will show that technical communicators are well suited to contribute to the development of online health information. Like other technical communicators, online health information developers must make rhetorical choices based on audience needs, function within specific social contexts, and work through challenges of writing, editing, and project management

Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms

ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies

Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK).

The second Signal Processing and Monitoring in Labor workshop gathered researchers who utilize promising new research strategies and initiatives to tackle the challenges of intrapartum fetal monitoring. The workshop included a series of lectures and discussions focusing on: new algorithms and techniques for cardiotocogoraphy (CTG) and electrocardiogram acquisition and analyses; the results of a CTG evaluation challenge comparing state-of-the-art computerized methods and visual interpretation for the detection of arterial cord pH <7.05 at birth; the lack of consensus about the role of intrapartum acidemia in the etiology of fetal brain injury; the differences between methods for CTG analysis "mimicking" expert clinicians and those derived from "data-driven" analyses; a critical review of the results from two randomized controlled trials testing the former in clinical practice; and relevant insights from modern physiology-based studies. We concluded that the automated algorithms performed comparably to each other and to clinical assessment of the CTG. However, the sensitivity and specificity urgently need to be improved (both computerized and visual assessment). Data-driven CTG evaluation requires further work with large multicenter datasets based on well-defined labor outcomes. And before first tests in the clinic, there are important lessons to be learnt from clinical trials that tested automated algorithms mimicking expert CTG interpretation. In addition, transabdominal fetal electrocardiogram monitoring provides reliable CTG traces and variability estimates; and fetal electrocardiogram waveform analysis is subject to promising new research. There is a clear need for close collaboration between computing and clinical experts. We believe that progress will be possible with multidisciplinary collaborative research

Assessing sectarian attitudes among Catholic adolescents in Scotland

Sectarianism is perceived as a serious issue in Scotland despite a lack of concrete evidence, according to the Advisory Group on Tackling Sectarianism. This paper addresses one of the gaps in knowledge, the attitudes of Catholic school pupils. Our research was designed to profile sectarian attitudes among a sample of Catholic school pupils in Scotland, using our own newly designed Scale of Catholic Sectarian Attitudes. The research assessed the influence of five sets of factors on shaping individual differences in sectarian attitudes: personal factors (sex and age), psychological factors (personality), religious factors (identity, belief, and practice), theological factors (exclusivism), and contextual factors (Catholic schools). The study draws on data provided by 797 13- to 15-year-old school pupils from schools in Scotland who self-identified as Roman Catholic. We offer a new tool for measuring attitudes to sectarianism and also findings that demonstrate that sectarian attitudes exist within the young Catholic community in Scotland and that this has possibly become part of a wider problem generated by the public visibility of religious diversity within an increasingly secular society. Further we find that Sectarian attitudes are higher among males than among females and are higher among nominal Catholics than among practising Catholics