"They aren't waiting for an SLP, they think 'what can I as a parent do now?'" course leaders' perceptions of AAC interventions targeting parents.

Parental interventions can help parents use strategies to support their child's language and communication development. The ComAlong courses are parental interventions that focus on responsive communication, enhanced milieu teaching, and augmentative and alternative communication. This interview study aimed to investigate the course leaders' perceptions of the three ComAlong courses, ComAlong Habilitation, ComAlong Developmental Language Disorder, and ComAlong Toddler, and to evaluate their experiences of the implementation of the courses. Qualitative content analysis was used to analyze the interview data. Thereafter, three categories resulted from the findings: Impact on the Family, A Great Course Concept, and Accessibility of the Courses. The results indicate that participants perceived that the courses had positive effects on both parents and themself. Furthermore, it was described that parents gained knowledge about communication and strategies in how to develop their child's communication; however, the courses were not accessible to all parents. The collaboration between the parents and course leaders improved, and course leaders viewed the courses as an important part of their work. The following factors had an impact on the implementation: several course leaders in the same workplace, support from colleagues and management, and recruitment of parents to the courses.

Impact of a Conformité Européenne (CE) Certification-Marked Medical Software Sensor on COVID-19 Pandemic Progression Prediction: Register-Based Study Using Machine Learning Methods.

BACKGROUND: To address the current COVID-19 and any future pandemic, we need robust, real-time, and population-scale collection and analysis of data. Rapid and comprehensive knowledge on the trends in reported symptoms in populations provides an earlier window into the progression of viral spread, and helps to predict the needs and timing of professional health care. OBJECTIVE: The objective of this study was to use a Conformité Européenne (CE)-marked medical online symptom checker service, Omaolo, and validate the data against the national demand for COVID-19-related care to predict the pandemic progression in Finland. METHODS: Our data comprised real-time Omaolo COVID-19 symptom checker responses (414,477 in total) and daily admission counts in nationwide inpatient and outpatient registers provided by the Finnish Institute for Health and Welfare from March 16 to June 15, 2020 (the first wave of the pandemic in Finland). The symptom checker responses provide self-triage information input to a medically qualified algorithm that produces a personalized probability of having COVID-19, and provides graded recommendations for further actions. We trained linear regression and extreme gradient boosting (XGBoost) models together with F-score and mutual information feature preselectors to predict the admissions once a week, 1 week in advance. RESULTS: Our models reached a mean absolute percentage error between 24.2% and 36.4% in predicting the national daily patient admissions. The best result was achieved by combining both Omaolo and historical patient admission counts. Our best predictor was linear regression with mutual information as the feature preselector. CONCLUSIONS: Accurate short-term predictions of COVID-19 patient admissions can be made, and both symptom check questionnaires and daily admissions data contribute to the accuracy of the predictions. Thus, symptom checkers can be used to estimate the progression of the pandemic, which can be considered when predicting the health care burden in a future pandemic.