You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["HealthCareActivity", "InjuryOrPoisoning", "BodySubstance", "IntellectualProduct", "AnatomicalStructure", "SpatialConcept", "Chemical", "Bacterium", "MedicalDevice", "Organization", "BiomedicalOccupationOrDiscipline", "Finding", "BiologicFunction", "Virus", "ResearchActivity", "ClinicalAttribute", "PopulationGroup", "Eukaryote", "BodySystem", "Food", "ProfessionalOrOccupationalGroup"].

Please return the output as a JSON object of the format: {"Virus": ["HIV", ...], "Bacterium": ["MRSA", ...], "AnatomicalStructure": ["Lung", ...], "BodySystem": ["CNS", ...], "BodySubstance": ["Serum", ...], "Finding": ["Headache", ...], "InjuryOrPoisoning": ["Fracture", ...], "BiologicFunction": ["Death", ...], "HealthCareActivity": ["Biopsy", ...], "ResearchActivity": ["Clinical trial", ...], "MedicalDevice": ["Lenses", ...], "SpatialConcept": ["Camps", ...], "BiomedicalOccupationOrDiscipline": ["Forensic medicine", ...], "Organization": ["WHO", ...], "ProfessionalOrOccupationalGroup": ["Provider", ...], "PopulationGroup": ["Swimmers", ...], "Chemical": ["Gold", ...], "Food": ["Rice", ...], "IntellectualProduct": ["RPAM", ...], "ClinicalAttribute": ["Biomarker", ...], "Eukaryote": ["Dogs", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Enhancing Doctors' Competencies in Communication With and Activation of Older Patients: The Promoting Active Aging (PRACTA) Computer-Based Intervention Study Demographic changes over the past decades call for the promotion of health and disease prevention for older patients, as well as strategies to enhance their independence, productivity, and quality of life. Our objective was to examine the effects of a computer-based educational intervention designed for general practitioners (GPs) to promote active aging. The Promoting Active Aging (PRACTA) study consisted of a baseline questionnaire, implementation of an intervention, and a follow-up questionnaire that was administered 1 month after the intervention. A total of 151 primary care facilities (response rate 151/767, 19.7%) and 503 GPs (response rate 503/996, 50.5%) agreed to participate in the baseline assessment. At the follow-up, 393 GPs filled in the questionnaires (response rate, 393/503, 78.1%), but not all of them took part in the intervention. The final study group of 225 GPs participated in 3 study conditions: e-learning (knowledge plus skills modelling, n=42), a pdf article (knowledge only, n=89), and control (no intervention, n=94). We measured the outcome as scores on the Patients Expectations Scale, Communication Scale, Attitude Toward Treatment and Health Scale, and Self-Efficacy Scale. GPs participating in e-learning demonstrated a significant rise in their perception of older patients ' expectations for disease explanation (Wald χ(2)=19.7, P<.001) and in perception of motivational aspect of older patients ' attitude toward treatment and health (Wald χ(2)=8.9, P=.03) in comparison with both the control and pdf article groups. We observed additional between- group differences at the level of statistical trend. GPs participating in the pdf article intervention demonstrated a decline in self-assessed communication, both at the level of global scoring (Wald χ(2)=34.5, P<.001) and at the level of 20 of 26 specific behaviors (all P<.05). Factors moderating the effects of the intervention were the number of patients per GP and the facility's organizational structure. Both methods were suitable, but in different areas and under different conditions. The key benefit of the pdf article intervention was raising doctors ' reflection on limitations in their communication skills, whereas e-learning was more effective in changing their perception of older patients ' proactive attitude, especially among GPs working in privately owned facilities and having a greater number of assigned patients. Although we did not achieve all expected effects of the PRACTA intervention, both its forms seem promising in terms of enhancing the competencies of doctors in communication with and activation of older patients.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["perception", "aging", "motivational", "Aging", "knowledge"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": [], "ClinicalAttribute": [], "Eukaryote": [], "Finding": [], "Food": [], "HealthCareActivity": ["Computer-Based Intervention Study", "computer-based educational intervention", "intervention", "PRACTA intervention", "PRACTA"], "InjuryOrPoisoning": [], "IntellectualProduct": ["Communication Scale", "follow-up questionnaire", "questionnaires", "disease explanation", "baseline questionnaire", "organizational structure", "Expectations Scale", "pdf article", "Attitude Toward Treatment and Health Scale", "modelling", "Self-Efficacy Scale"], "MedicalDevice": [], "Organization": ["primary care facilities", "privately owned facilities"], "PopulationGroup": ["Older", "older"], "ProfessionalOrOccupationalGroup": ["Doctors\u0027", "GP", "doctors", "general practitioners", "GPs"], "ResearchActivity": ["follow-up", "baseline assessment"], "SpatialConcept": ["areas"], "Virus": []}