Index of Problematic Online Experiences: Item Characteristics and Correlation with Negative Symptomatology

This exploratory study aimed to develop and test a quick, easily administered instrument, the Index of Problematic Online Experiences (I-POE). The goal of the I-POE extends beyond assessing for Internet overuse to broadly assess problematic Internet use across several domains and activities. Data was collected from 563 college students from a Northern New England university using an online survey methodology. Results indicated the I-POE has adequate construct validity and is highly correlated with a variety of relevant constructs: depression, anger=irritability, tension-reduction behavior, sexual concerns, and dysfunctional sexual behavior as measured by the Trauma Symptom Inventory; as well as amount of Internet use and permissive attitudes toward engaging in a variety of sexual activities. Early flagging of online experiences could mitigate the negative effects associated with problematic use. The I-POE, as an easy-to-administer, short screening index, holds promise in this regard. Initial testing of the instrument points to its utility in identifying persons who are experiencing a broad range of Internet-related problems

The Complexity of Language and Learning: Deconstructing Teachers\u27 Conceptions of Academic Language

“Academic language” is a term that is thrown around frequently in educational circles, particularly in recent years. Whether in pre-service teacher education with candidates and cooperating teachers preparing for the widely required Teacher Performance Assessment (edTPA; Stanford Center for Assessment, Learning, and Equity, 2016), or in-service teachers grappling with the implementation of the Common Core Standards (National Governors Association, 2010), academic language has become de rigueur a jargon term required for a number of current classroom, school, and university initiatives. But what is academic language

Assessment and Response Protocol

The purpose of this protocol is to support clinicians to use PCOC’s assessment and response framework to identify, respond to and communicate patient, and family/carer’s needs. The resource contains strategies for embedding the assessment tools into routine practice

Comparison of Costs of Home and Facility-based Basic Obstetric Care in Rural Bangladesh

This study compared the costs of providing antenatal, delivery and postnatal care in the home and in a basic obstetric facility in rural Bangladesh. The average costs were estimated by interviewing midwives and from institutional records. The main determinants of cost in each setting were also assessed. The cost of basic obstetric care in the home and in a facility was very similar, although care in the home was cheaper. Deliveries in the home took more time but this was offset by the capital costs associated with facility-based care. As use-rates increase, deliveries in a facility will become cheaper. Antenatal and postnatal care was much cheaper to provide in the facility than in the home. Facility-based delivery care is likely to be a cheaper and more feasible method for the care provider as demand rises. In settings where skilled attendance rates are very low, home-based care will be cheaper

Robust optimization of SVM hyperparameters in the classification of bioactive compounds

Background: Support Vector Machine has become one of the most popular machine learning tools used in vir - tual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its application requires the optimization of the hyperparameters with which the assessment is being run, particularly the C and γ values. The optimization requirement in turn, establishes the need to develop fast and effective approaches to the optimization procedure, providing the best predictive power of the constructed model. Results: In this study, we investigated the Bayesian and random search optimization of Support Vector Machine hyperparameters for classifying bioactive compounds. The effectiveness of these strategies was compared with the most popular optimization procedures—grid search and heuristic choice. We demonstrated that Bayesian optimiza- tion not only provides better, more efficient classification but is also much faster—the number of iterations it required for reaching optimal predictive performance was the lowest out of the all tested optimization methods. Moreover, for the Bayesian approach, the choice of parameters in subsequent iterations is directed and justified; therefore, the results obtained by using it are constantly improved and the range of hyperparameters tested provides the best over - all performance of Support Vector Machine. Additionally, we showed that a random search optimization of hyperpa- rameters leads to significantly better performance than grid search and heuristic-based approaches. Conclusions: The Bayesian approach to the optimization of Support Vector Machine parameters was demonstrated to outperform other optimization methods for tasks concerned with the bioactivity assessment of chemical com- pounds. This strategy not only provides a higher accuracy of classification, but is also much faster and more directed than other approaches for optimization. It appears that, despite its simplicity, random search optimization strategy should be used as a second choice if Bayesian approach application is not feasible

The influence of negative training set size on machine learning-based virtual screening

BACKGROUND: The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. RESULTS: The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. CONCLUSIONS: In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening

Development and Validation of a Routine Session-by-Session Experience Measure for Youth Mental Health Services:My Youth Mental Health Session Experience (MySE)

Purpose: The ‘My youth mental health Session Experience’ (MySE) measure was developed by headspace, Australia’s National Youth Mental Health Foundation, in collaboration with young people, for use as a routine session experience measure across its national centre service network. The measure fills a gap in measures needed to implement measurement-informed care in youth mental health care. Participants and Methods: Routinely collected data from 37,201 young people aged 12 to 25 years who commenced an episode of care at one of the 150 headspace centres between 1 July 2021 and 30 June 2022 were used to validate the five-item measure. Results: MySE demonstrated high internal consistency invariant over age and gender groups. There was one latent factor of session experience that all MySE items relate to, although this factor does not adequately capture all the information present in the individual items. A significant age effect showed that young adults reported more positive session experiences than adolescents. Conclusion: MySE demonstrated strong psychometric properties and is suitable for use in youth mental health care as a routine session-by-session experience measure. Such measures are needed to routinely inform clinicians of how young people are experiencing their treatment sessions, thereby contributing to better retention, engagement, and client outcomes through measurement-informed care.</p