scholarly journals Assessing Practice Habits: A Study of Collegiate Instrumental Teachers’ Estimation of Students’ Practice Habits Versus Students’ Self-Report

2020 ◽  
Vol 9 ◽  
pp. 17-28
Author(s):  
Chooi Wee Lau
Keyword(s):  
Undergraduate Students ◽  
Music Performance ◽  
Mental Practice ◽  
Self Report ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Percent Agreement ◽  
Private Lesson ◽  
Key Indicators ◽  
Practice Habits

This survey research aims to assess the collegiate instrumental teachers’ ability to estimate students’ practice habits in the practice room based on the students’ performance during the instrumental lesson and to collect collegiate instrumental teachers’ suggestions on estimating students’ practice habits in the practice room. A questionnaire in two forms was designed for 15 collegiate instrumental teachers and 30 music performance undergraduate students who were selected through a convenience sampling approach. The percent agreement (PA) and Cohen’s kappa (𝜿) were utilised to examine the inter-rater reliability between the results of both participants on the practice habits that focus on the practice time, practice sessions, goal setting, focused attention, mental practice, technique practice, metronome practice, practise with an electronic tuner, and practise with other practice strategies. The low average results, 31.50% on the percent agreement and .0437 on the Cohen’s kappa revealed that collegiate instrumental teachers cannot effectively estimate their students’ practice habits in the practice room based on the students’ performance during the instrumental lesson. However, an interesting observation was made from the suggestions given by the teachers, that is, the importance of communication of practice habits as well as observation of them in the private lesson studio. To improve, a system that teaches the key indicators of estimating students’ practice habits or a training package or method to observe students’ use of practice habits in the practice room is recommended to develop for future teachers.

Coping Style as a Mediator of the Relationship Between Depressive Symptoms and Deliberate Self-Harm

Crisis ◽  
2011 ◽  
Vol 32 (5) ◽  
pp. 272-279 ◽  
Author(s):  
Allison S. Christian ◽  
Kristen M. McCabe
Keyword(s):  
Depressive Symptoms ◽  
Undergraduate Students ◽  
Cross Sectional Study ◽  
Self Report ◽  
Hierarchical Regression ◽  
Cross Sectional ◽  
Deliberate Self Harm ◽  
Self Blame ◽  
Self Harm ◽  
The Relationship

Background: Deliberate self-harm (DSH) occurs with high frequency among clinical and nonclinical youth populations. Although depression has been consistently linked with the behavior, not all depressed individuals engage in DSH. Aims: The current study examined maladaptive coping strategies (i.e., self-blame, distancing, and self-isolation) as mediators between depression and DSH among undergraduate students. Methods: 202 students from undergraduate psychology courses at a private university in Southern California (77.7% women) completed anonymous self-report measures. Results: A hierarchical regression model found no differences in DSH history across demographic variables. Among coping variables, self-isolation alone was significantly related to DSH. A full meditational model was supported: Depressive symptoms were significantly related to DSH, but adding self-isolation to the model rendered the relationship nonsignificant. Limitations: The cross-sectional study design prevents determination of whether a casual relation exists between self-isolation and DSH, and obscures the direction of that relationship. Conclusions: Results suggest targeting self-isolation as a means of DSH prevention and intervention among nonclinical, youth populations.

Ethical Issues Relevant to the Assessment of Suicide Risk in Nonclinical Research Settings

Crisis ◽  
2012 ◽  
Vol 33 (1) ◽  
pp. 54-59 ◽  
Author(s):  
Carolyn M. Wilson ◽  
Bruce K. Christensen
Keyword(s):  
Undergraduate Students ◽  
Suicide Risk ◽  
Ethical Issues ◽  
Current Paper ◽  
Self Report ◽  
Ethical Guidelines ◽  
Schizotypal Personality ◽  
Increased Risk ◽  
Genetic Features ◽  
Conducting Research

Background: Our laboratory recently confronted this issue while conducting research with undergraduate students at the University of Waterloo (UW). Although our main objective was to examine cognitive and genetic features of individuals with schizotypal personality disorder (SPD), the study protocol also entailed the completion of various self-report measures to identify participants deemed at increased risk for suicide. Aims and Methods: This paper seeks to review and discuss the relevant ethical guidelines and legislation that bear upon a psychologist’s obligation to further assess and intervene when research participants reveal that they are at increased risk for suicide. Results and Conclusions: In the current paper we argue that psychologists are ethically impelled to assess and appropriately intervene in cases of suicide risk, even when such risk is revealed within a research context. We also discuss how any such obligation may potentially be modulated by the research participant’s expectations of the role of a psychologist, within such a context. Although the focus of the current paper is on the ethical obligations of psychologists, specifically those practicing within Canada, the relevance of this paper extends to all regulated health professionals conducting research in nonclinical settings.

Does Dispositional Mindfulness Predict the Development of Grit?

2018 ◽  
Vol 39 (2) ◽  
pp. 76-87 ◽  
Author(s):  
Buaphrao Raphiphatthana ◽  
Paul Jose ◽  
Karen Salmon
Keyword(s):  
Undergraduate Students ◽  
Online Survey ◽  
Cross Sectional Study ◽  
Self Report ◽  
Dispositional Mindfulness ◽  
Cross Sectional ◽  
Report Data ◽  
Self Report Data ◽  
Acting With Awareness ◽  
Mindfulness Facets

Abstract. Grit, that is, perseverance and passion for long-term goals, is a novel construct that has gained attention in recent years ( Duckworth, Peterson, Matthews, & Kelly, 2007 ). To date, little research has been performed with the goal of identifying the antecedents of grit. Thus, in order to fill this gap in the literature, self-report data were collected to examine whether mindfulness, a mindset of being-in-the-present in a nonjudgmental way, plays a role in fostering grittiness. Three hundred and forty-three undergraduate students completed an online survey once in a cross-sectional study, and of these, 74 students completed the survey again 4.5 months later. Although the cross-sectional analyses identified a number of positive associations between mindfulness and grit, the longitudinal analysis revealed that the mindfulness facets of acting with awareness and non-judging were the most important positive predictors of grit 4.5 months later. This set of findings offers implications for future grit interventions.

scholarly journals An update on image forming methods: structure analysis and Gestalt evaluation of images from rocket lettuce with shading, N supply, organic or mineral fertilization, and biodynamic preparations

2021 ◽  
Author(s):  
Miriam Athmann ◽  
Roya Bornhütter ◽  
Nicolaas Busscher ◽  
Paul Doesburg ◽  
Uwe Geier ◽  
...  
Keyword(s):  
Copper Chloride ◽  
Organic Fertilizer ◽  
Structural Features ◽  
Fertilizer Treatment ◽  
Friedman Test ◽  
Mineral Fertilization ◽  
Cohen’S Kappa ◽  
N Supply ◽  
Cohen's Kappa ◽  
Forming Methods

AbstractIn the image forming methods, copper chloride crystallization (CCCryst), capillary dynamolysis (CapDyn), and circular chromatography (CChrom), characteristic patterns emerge in response to different food extracts. These patterns reflect the resistance to decomposition as an aspect of resilience and are therefore used in product quality assessment complementary to chemical analyses. In the presented study, rocket lettuce from a field trial with different radiation intensities, nitrogen supply, biodynamic, organic and mineral fertilization, and with or without horn silica application was investigated with all three image forming methods. The main objective was to compare two different evaluation approaches, differing in the type of image forming method leading the evaluation, the amount of factors analyzed, and the deployed perceptual strategy: Firstly, image evaluation of samples from all four experimental factors simultaneously by two individual evaluators was based mainly on analyzing structural features in CapDyn (analytical perception). Secondly, a panel of eight evaluators applied a Gestalt evaluation imbued with a kinesthetic engagement of CCCryst patterns from either fertilization treatments or horn silica treatments, followed by a confirmatory analysis of individual structural features. With the analytical approach, samples from different radiation intensities and N supply levels were identified correctly in two out of two sample sets with groups of five samples per treatment each (Cohen’s kappa, p = 0.0079), and the two organic fertilizer treatments were differentiated from the mineral fertilizer treatment in eight out of eight sample sets with groups of three manure and two minerally fertilized samples each (Cohen’s kappa, p = 0.0048). With the panel approach based on Gestalt evaluation, biodynamic fertilization was differentiated from organic and mineral fertilization in two out of two exams with 16 comparisons each (Friedman test, p < 0.001), and samples with horn silica application were successfully identified in two out of two exams with 32 comparisons each (Friedman test, p < 0.001). Further research will show which properties of the food decisive for resistance to decomposition are reflected by analytical and Gestalt criteria, respectively, in CCCryst and CapDyn images.

scholarly journals Prediction of Streptococcus uberis clinical mastitis treatment success in dairy herds by means of mass spectrometry and machine-learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexandre Maciel-Guerra ◽  
Necati Esener ◽  
Katharina Giebel ◽  
Daniel Lea ◽  
Martin J. Green ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Treatment Success ◽  
Clinical Mastitis ◽  
Supervised Machine Learning ◽  
Structural Protein ◽  
Cohen’S Kappa ◽  
Maldi Tof ◽  
Streptococcus Uberis ◽  
Cohen's Kappa

AbstractStreptococcus uberis is one of the leading pathogens causing mastitis worldwide. Identification of S. uberis strains that fail to respond to treatment with antibiotics is essential for better decision making and treatment selection. We demonstrate that the combination of supervised machine learning and matrix-assisted laser desorption ionization/time of flight (MALDI-TOF) mass spectrometry can discriminate strains of S. uberis causing clinical mastitis that are likely to be responsive or unresponsive to treatment. Diagnostics prediction systems trained on 90 individuals from 26 different farms achieved up to 86.2% and 71.5% in terms of accuracy and Cohen’s kappa. The performance was further increased by adding metadata (parity, somatic cell count of previous lactation and count of positive mastitis cases) to encoded MALDI-TOF spectra, which increased accuracy and Cohen’s kappa to 92.2% and 84.1% respectively. A computational framework integrating protein–protein networks and structural protein information to the machine learning results unveiled the molecular determinants underlying the responsive and unresponsive phenotypes.

scholarly journals The accuracy of initial diagnoses in coma: an observational study in 835 patients with non-traumatic disorder of consciousness

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Maximilian Lutz ◽  
Martin Möckel ◽  
Tobias Lindner ◽  
Christoph J. Ploner ◽  
Mischa Braun ◽  
...  
Keyword(s):  
Emergency Care ◽  
Final Diagnosis ◽  
Class I ◽  
Prehospital Emergency Care ◽  
Unknown Etiology ◽  
Care Providers ◽  
Cohen’S Kappa ◽  
Disorder Of Consciousness ◽  
Cohen's Kappa ◽  
Suspected Diagnosis

Abstract Background Management of patients with coma of unknown etiology (CUE) is a major challenge in most emergency departments (EDs). CUE is associated with a high mortality and a wide variety of pathologies that require differential therapies. A suspected diagnosis issued by pre-hospital emergency care providers often drives the first approach to these patients. We aim to determine the accuracy and value of the initial diagnostic hypothesis in patients with CUE. Methods Consecutive ED patients presenting with CUE were prospectively enrolled. We obtained the suspected diagnoses or working hypotheses from standardized reports given by prehospital emergency care providers, both paramedics and emergency physicians. Suspected and final diagnoses were classified into I) acute primary brain lesions, II) primary brain pathologies without acute lesions and III) pathologies that affected the brain secondarily. We compared suspected and final diagnosis with percent agreement and Cohen’s Kappa including sub-group analyses for paramedics and physicians. Furthermore, we tested the value of suspected and final diagnoses as predictors for mortality with binary logistic regression models. Results Overall, suspected and final diagnoses matched in 62% of 835 enrolled patients. Cohen’s Kappa showed a value of κ = .415 (95% CI .361–.469, p < .005). There was no relevant difference in diagnostic accuracy between paramedics and physicians. Suspected diagnoses did not significantly interact with in-hospital mortality (e.g., suspected class I: OR .982, 95% CI .518–1.836) while final diagnoses interacted strongly (e.g., final class I: OR 5.425, 95% CI 3.409–8.633). Conclusion In cases of CUE, the suspected diagnosis is unreliable, regardless of different pre-hospital care providers’ qualifications. It is not an appropriate decision-making tool as it neither sufficiently predicts the final diagnosis nor detects the especially critical comatose patient. To avoid the risk of mistriage and unnecessarily delayed therapy, we advocate for a standardized diagnostic work-up for all CUE patients that should be triggered by the emergency symptom alone and not by any suspected diagnosis.

scholarly journals Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

2021 ◽  
Vol 11 (6) ◽  
pp. 2723
Author(s):  
Fatih Uysal ◽  
Fırat Hardalaç ◽  
Ozan Peker ◽  
Tolga Tolunay ◽  
Nil Tokgöz
Keyword(s):  
Deep Learning ◽  
Performance Test ◽  
The Body ◽  
Test Accuracy ◽  
Cohen’S Kappa ◽  
X Ray ◽  
Cohen's Kappa ◽  
Auc Value ◽  
Magnetic Resonance Imaging Mri ◽  
Fully Connected

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.

scholarly journals The South African dysphagia screening tool (SADS): A screening tool for a developing context

2016 ◽  
Vol 63 (1) ◽  
Author(s):  
Calli Ostrofsky ◽  
Jaishika Seedat
Keyword(s):  
At Risk ◽  
Acute Stroke ◽  
South African ◽  
Screening Tool ◽  
Diagnostic Assessment ◽  
The South ◽  
Stroke Patients ◽  
Cohen’S Kappa ◽  
Cohen's Kappa ◽  
Government Hospitals

Background: Notwithstanding its value, there are challenges and limitations to implementing a dysphagia screening tool from a developed contexts in a developing context. The need for a reliable and valid screening tool for dysphagia that considers context, systemic rules and resources was identified to prevent further medical compromise, optimise dysphagia prognosis and ultimately hasten patients’ return to home or work.Methodology: To establish the validity and reliability of the South African dysphagia screening tool (SADS) for acute stroke patients accessing government hospital services. The study was a quantitative, non-experimental, correlational cross-sectional design with a retrospective component. Convenient sampling was used to recruit 18 speech-language therapists and 63 acute stroke patients from three South African government hospitals. The SADS consists of 20 test items and was administered by speech-language therapists. Screening was followed by a diagnostic dysphagia assessment. The administrator of the tool was not involved in completing the diagnostic assessment, to eliminate bias and prevent contamination of results from screener to diagnostic assessment. Sensitivity, validity and efficacy of the screening tool were evaluated against the results of the diagnostic dysphagia assessment. Cohen’s kappa measures determined inter-rater agreement between the results of the SADS and the diagnostic assessment.Results and conclusion: The SADS was proven to be valid and reliable. Cohen’s kappa indicated a high inter-rater reliability and showed high sensitivity and adequate specificity in detecting dysphagia amongst acute stroke patients who were at risk for dysphagia. The SADS was characterised by concurrent, content and face validity. As a first step in establishing contextual appropriateness, the SADS is a valid and reliable screening tool that is sensitive in identifying stroke patients at risk for dysphagia within government hospitals in South Africa.

JUUL E-Cigarette Quit Attempts and Cessation Perceptions in College Student JUUL E-Cigarette Users

2020 ◽  
pp. 089011712098240
Author(s):  
Kim Pulvers ◽  
John B. Correa ◽  
Paul Krebs ◽  
Omar El Shahawy ◽  
Crystal Marez ◽  
...  
Keyword(s):  
College Student ◽  
Undergraduate Students ◽  
Online Survey ◽  
Self Report ◽  
Cross Sectional ◽  
Quit Attempt ◽  
Lower Confidence ◽  
Perceived Difficulty ◽  
Quit Attempts ◽  
History Of

Purpose: This study describes the frequency of JUUL e-cigarette (referred to as JUUL) quit attempts and identifies characteristics associated with confidence in quitting and perceived difficulty quitting JUUL. Design: Cross-sectional study from a self-administered online survey. Setting: Two public southern California universities. Participants: A total of 1,001 undergraduate students completed the survey from February to May 2019. Measures: Self-report measures about JUUL included use, history of quit attempts, time to first use, perceived difficulty with cessation/reduction, and confidence in quitting. Analysis: Binary logistic regressions were used to identify demographic and tobacco-related behavioral correlates of JUUL cessation-related perceptions and behaviors. Results: Nearly half of ever-JUUL users (47.8%) reported a JUUL quit attempt. Adjusting for demographic factors and other tobacco product use, shorter time to first JUUL use after waking was associated with lower confidence in quitting JUUL (aOR = 0.02, 0.00-0.13) and greater perceived difficulty in quitting JUUL (aOR = 8.08, 2.15-30.35). Previous JUUL quit attempt history was also associated with greater odds of perceived difficulty quitting JUUL (aOR = 5.97, 1.74-20.53). Conclusions: History of JUUL quit attempts among college students was common. Those who had previously tried quitting were more likely to perceive difficulty with cessation. Time to first JUUL use, a marker of dependence, was linked with greater perceived cessation difficulty and lower confidence in quitting. These findings suggest that there is a need for cessation and relapse prevention support for college student JUUL users.

Deep Learning–Based Automated Thrombolysis in Cerebral Infarction Scoring: A Timely Proof-of-Principle Study

Stroke ◽  
2021 ◽  
Author(s):  
Maximilian Nielsen ◽  
Moritz Waldmann ◽  
Andreas M. Frölich ◽  
Fabian Flottmann ◽  
Evelin Hristova ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cerebral Infarction ◽  
Digital Subtraction Angiography ◽  
Information Integration ◽  
Mechanical Thrombectomy ◽  
Model Updating ◽  
Digital Subtraction ◽  
Cohen’S Kappa ◽  
Expert Rating ◽  
Cohen's Kappa

Background and Purpose: Mechanical thrombectomy is an established procedure for treatment of acute ischemic stroke. Mechanical thrombectomy success is commonly assessed by the Thrombolysis in Cerebral Infarction (TICI) score, assigned by visual inspection of X-ray digital subtraction angiography data. However, expert-based TICI scoring is highly observer-dependent. This represents a major obstacle for mechanical thrombectomy outcome comparison in, for instance, multicentric clinical studies. Focusing on occlusions of the M1 segment of the middle cerebral artery, the present study aimed to develop a deep learning (DL) solution to automated and, therefore, objective TICI scoring, to evaluate the agreement of DL- and expert-based scoring, and to compare corresponding numbers to published scoring variability of clinical experts. Methods: The study comprises 2 independent datasets. For DL system training and initial evaluation, an in-house dataset of 491 digital subtraction angiography series and modified TICI scores of 236 patients with M1 occlusions was collected. To test the model generalization capability, an independent external dataset with 95 digital subtraction angiography series was analyzed. Characteristics of the DL system were modeling TICI scoring as ordinal regression, explicit consideration of the temporal image information, integration of physiological knowledge, and modeling of inherent TICI scoring uncertainties. Results: For the in-house dataset, the DL system yields Cohen’s kappa, overall accuracy, and specific agreement values of 0.61, 71%, and 63% to 84%, respectively, compared with the gold standard: the expert rating. Values slightly drop to 0.52/64%/43% to 87% when the model is, without changes, applied to the external dataset. After model updating, they increase to 0.65/74%/60% to 90%. Literature Cohen’s kappa values for expert-based TICI scoring agreement are in the order of 0.6. Conclusions: The agreement of DL- and expert-based modified TICI scores in the range of published interobserver variability of clinical experts highlights the potential of the proposed DL solution to automated TICI scoring.

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