PREDICTIVE VALUE OF AN ONLINE ASSESSMENT TOOL EVALUATING FOREKNOWLEDGE IN MATHEMATICS AND SCIENCES OF ASPIRANT UNIVERSITY COLLEGE STUDENTS

2016 ◽  
Author(s):  
Karolien Van den Bergh ◽  
Lut Gielen ◽  
Margo Mahieu ◽  
Linde Vanlommel
Keyword(s):  
College Students ◽  
Predictive Value ◽  
Assessment Tool ◽  
Online Assessment ◽  
University College

An Overview of the Frameworks for Measuring the Digital Competencies of College Students

2021 ◽  
pp. 259-282
Author(s):  
Vladimir M. Simović ◽  
Ivana S. Domazet
Keyword(s):  
College Students ◽  
Formal Education ◽  
Assessment Tool ◽  
Assessment Tools ◽  
Online Assessment ◽  
Competence Assessment ◽  
Digital Competence ◽  
Digital Competencies ◽  
Entrepreneurial Competencies ◽  
Key Aspects

The purpose of this chapter is to analyze the options for measuring the digital entrepreneurial competencies the college students acquire during the course of their formal education. The chapter examines the key aspects of various digital competence-related frameworks and proposes the development of a new methodology that will be focused on the digital entrepreneurial competencies of the students. This chapter proposes the development of the corresponding online assessment tool which could serve to measure the level of the acquired competencies by the students. The findings presented in this chapter may apply to other areas as well. The goal is to develop a set of competence assessment tools that could effectively determine the level of competencies the students acquire during the course of their formal studies and enable the formulation of adequate corrective measures in the curriculum plan.

scholarly journals A machine learning approach for predicting suicidal thoughts and behaviours among college students

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Melissa Macalli ◽  
Marie Navarro ◽  
Massimiliano Orri ◽  
Marie Tournier ◽  
Rodolphe Thiébaut ◽  
...  
Keyword(s):  
Mental Health ◽  
College Students ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Screening Tools ◽  
Receiver Operating Curve ◽  
Depression Symptoms ◽  
Suicidal Thoughts ◽  
One Year

AbstractSuicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013–2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviours. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviours in a community sample of college students.

Identifying children at high risk for infection-related decompensation using a predictive emergency department-based electronic assessment tool

Diagnosis ◽  
2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Robert J. Sepanski ◽  
Arno L. Zaritsky ◽  
Sandip A. Godambe
Keyword(s):  
Emergency Department ◽  
High Risk ◽  
Positive Predictive Value ◽  
Firing Rate ◽  
Predictive Value ◽  
Assessment Tool ◽  
Predictive Tool ◽  
High Severity ◽  
Electronic Assessment ◽  
Ed Visits

AbstractObjectivesElectronic alert systems to identify potential sepsis in children presenting to the emergency department (ED) often either alert too frequently or fail to detect earlier stages of decompensation where timely treatment might prevent serious outcomes.MethodsWe created a predictive tool that continuously monitors our hospital’s electronic health record during ED visits. The tool incorporates new standards for normal/abnormal vital signs based on data from ∼1.2 million children at 169 hospitals. Eighty-two gold standard (GS) sepsis cases arising within 48 h were identified through retrospective chart review of cases sampled from 35,586 ED visits during 2012 and 2014–2015. An additional 1,027 cases with high severity of illness (SOI) based on 3 M’s All Patient Refined – Diagnosis-Related Groups (APR-DRG) were identified from these and 26,026 additional visits during 2017. An iterative process assigned weights to main factors and interactions significantly associated with GS cases, creating an overall “score” that maximized the sensitivity for GS cases and positive predictive value for high SOI outcomes.ResultsTool implementation began August 2017; subsequent improvements resulted in 77% sensitivity for identifying GS sepsis within 48 h, 22.5% positive predictive value for major/extreme SOI outcomes, and 2% overall firing rate of ED patients. The incidence of high-severity outcomes increased rapidly with tool score. Admitted alert positive patients were hospitalized nearly twice as long as alert negative patients.ConclusionsOur ED-based electronic tool combines high sensitivity in predicting GS sepsis, high predictive value for physiologic decompensation, and a low firing rate. The tool can help optimize critical treatments for these high-risk children.

Bleeding after surgery and application bleeding assessment tool to surgical patients in Hanoi Medicine University Hospital

2021 ◽  
Vol 25 (2) ◽  
pp. 94-101
Author(s):  
Thi Minh Khue Nguyen ◽  
Quang Tung Nguyen
Keyword(s):  
Predictive Value ◽  
Assessment Tool ◽  
Bleeding Risk ◽  
University Hospital ◽  
Surgical Patients ◽  
Risk Of Bleeding ◽  
Patients At Risk ◽  
Abnormal Bleeding ◽  
Medical University ◽  
Bleeding History

Objectives: Describe bleeding characteristics and evaluate the correlation between surgical-related bleeding and bleeding risk according by ISTH – BATs. Methods: Research was conducted on 340 surgical patients at Hanoi Medical University Hospital. Results: The percentage of patients with bleeding during and after surgery is 13.5%. The proportion of patients at risk of bleeding according to BATs is 1.8%. There was a correlation between bleeding risk according to ISTH - BAT with bleeding status during and after surgery with p = 0.004. The positive predictive value of ISTH - BATs is 66.7%, negative predictive value is 87.4%, the sensitivity is 8.7%, the specificity is 99.3%. Conclusions: Surgery has a high risk of abnormal bleeding. Bleeding history has important implications in assessing bleeding risk during and after surgery. The ISTH - BATs is a bleeding history assessment tool that can be used to assess the risk of bleeding before surgery.

scholarly journals A New Rapid Approach for Predicting Death in Coronavirus Patients: The Development and Validation of the COVID-19 Risk-Score in Fars Province (CRSF)

2022 ◽  
Author(s):  
Mehrdad Sharifi ◽  
Mohammad Hossein Khademian ◽  
Razieh Sadat Mousavi-Roknabadi ◽  
Vahid Ebrahimi ◽  
Robab Sadegh
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Predictive Value ◽  
Assessment Tool ◽  
Fars Province ◽  
Risk Of Mortality ◽  
Risk Patients ◽  
Independent Risk Factors ◽  
Development And Validation ◽  
Underlying Diseases

Background:Patients who are identified to be at a higher risk of mortality from COVID-19 should receive better treatment and monitoring. This study aimed to propose a simple yet accurate risk assessment tool to help decision-making in the management of the COVID-19 pandemic. Methods: From Jul to Nov 2020, 5454 patients from Fars Province, Iran, diagnosed with COVID-19 were enrolled. A multiple logistic regression model was trained on one dataset (training set: n=4183) and its prediction performance was assessed on another dataset (testing set: n=1271). This model was utilized to develop the COVID-19 risk-score in Fars (CRSF). Results: Five final independent risk factors including gender (male: OR=1.37), age (60-80: OR=2.67 and >80: OR=3.91), SpO2 (≤85%: OR=7.02), underlying diseases (yes: OR=1.25), and pulse rate (<60: OR=2.01 and >120: OR=1.60) were significantly associated with in-hospital mortality. The CRSF formula was obtained using the estimated regression coefficient values of the aforementioned factors. The point values for the risk factors varied from 2 to 19 and the total CRSF varied from 0 to 45. The ROC analysis showed that the CRSF values of ≥15 (high-risk patients) had a specificity of 73.5%, sensitivity of 76.5%, positive predictive value of 23.2%, and negative predictive value (NPV) of 96.8% for the prediction of death (AUC=0.824, P<0.0001). Conclusion:This simple CRSF system, which has a high NPV,can be useful for predicting the risk of mortality in COVID-19 patients. It can also be used as a disease severity indicator to determine triage level for hospitalization.

Development of a Screen for Ongoing Intimate Partner Violence

2003 ◽  
Vol 18 (2) ◽  
pp. 131-141 ◽  
Author(s):  
Steve J. Weiss ◽  
Amy A. Ernst ◽  
Elaine Cham ◽  
Todd G. Nick
Keyword(s):  
Emergency Department ◽  
Intimate Partner Violence ◽  
Positive Predictive Value ◽  
Partner Violence ◽  
Predictive Value ◽  
Spouse Abuse ◽  
Assessment Tool ◽  
Positive Likelihood Ratio ◽  
Intimate Partner ◽  
Survey Tool

A five-question Ongoing Abuse Screen (OAS) was developed to evaluate ongoing intimate partner violence. Our hypothesis was that the OAS was more accurate and more likely to reflect ongoing intimate partner violence than the AAS when compared to the Index of Spouse Abuse (ISA). The survey included the ISA, the OAS, and the AAS. During the busiest emergency department hours, a sampling of 856 patients completed all aspects of the survey tool. Comparisons were made between the two scales and the ISA. The accuracy, positive predictive value, and positive likelihood ratio were 84%, 58%, and 6.0 for the OAS and 59%, 33%, and 2.0 for the AAS. The OAS was more accurate, had a better positive predictive value, and was three times more likely to detect victims of ongoing intimate partner violence than the AAS. Because the OAS was still not accurate enough, we developed a new screen, based on the ISA, titled the Ongoing Violence Assessment Tool (OVAT).

Sign in / Sign up

Export Citation Format

Share Document