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dc.contributor.authorChelyshkova, M. B.en
dc.contributor.authorSemenova, T. V.en
dc.contributor.authorNaydenova, N. N.en
dc.contributor.authorDorozhkin, E. M.en
dc.contributor.authorMalygin, A. A.en
dc.contributor.authorAkhunov, V. V.en
dc.coverage.spatialRSVPUen
dc.coverage.spatialSCOPUSen
dc.date.accessioned2019-07-17T10:00:13Z-
dc.date.available2019-07-17T10:00:13Z-
dc.date.issued2018-
dc.identifier.issn2516-3507-
dc.identifier.otherhttp://www.ejgm.co.uk/pdf-93469-27490?filename=_ross-analysis of big.pdfpdf
dc.identifier.otherhttps://www.scopus.com/record/display.uri?origin=resultslist&eid=2-s2.0-85056579992scopus_url
dc.identifier.urihttps://elar.rsvpu.ru/handle/123456789/28100-
dc.description.abstractObjective: The relevance of this study is due to the mass accreditation of health professionals that is developing in Russia, which requires innovative measurement tools and opens new opportunities for a well-founded cross-analysis of specialists’ professional readiness quality. Purpose of the study: The purpose of this article is to present approved methodical approaches to the transformation of accreditation data into a format suitable for secondary analysis of medical schools graduates quality based on the requirements of Professional Standards. Method: The leading methods of secondary data analysis are: a) codification of indicators in the primary data accumulation array; b) statistical processing of study results (evaluation of the relationships between the arrays of primary data accumulation and instrumental data, the correlation of test scores obtained by accreditation results with the labor functions of Professional Standards); c) the creation of representative samples for data analysis. The implementation of methods is carried out in the mode of working with arrays of big data, which also uses the method of cross-analysis to identify additional factors that affect to specialists’ professional readiness quality. Results: As a results of the research, there were: 1) approaches to the codification of data in the array and their secondary analysis were developed; 2) three samples were constructed with an estimation of representativeness for different strata, including subjects, assignments and corresponding labor functions; 3) the matrix of primary data in the specialty “Pediatrics” was verified using the example of the results of students from 50 medical universities in Russia. Conclusion: Approbation of methods of secondary data analysis conducted on representative samples of the subjects showed the effectiveness of the developed approaches that should be used when analyzing large data sets in the procedures of certification or accreditation. The materials of the article can be useful for specialists in the field of assessing the quality of education or assessing the professional readiness of health professionals, managers, professors and pedagogical staff of medical schools, specialists of centers for independent assessment of qualifications. © 2018, Modestum Ltd.. All rights reserved.en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.publisherModestum Limiteden
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.sourceElectronic Journal of General Medicineen
dc.subjectCROSS-ANALYSISen
dc.subjectLABOR FUNCTIONSen
dc.subjectLARGE DATAen
dc.subjectPRIMARY AND SECONDARY ANALYSISen
dc.subjectREPRESENTATIVE SAMPLESen
dc.subjectADULTen
dc.subjectARTICLEen
dc.subjectCASE REPORTen
dc.subjectCERTIFICATIONen
dc.subjectCLINICAL ARTICLEen
dc.subjectDATA ANALYSISen
dc.subjectFEMALEen
dc.subjectHUMANen
dc.subjectHUMAN EXPERIMENTen
dc.subjectMALEen
dc.subjectMANAGERen
dc.subjectMEDICAL SCHOOLen
dc.subjectPEDIATRICSen
dc.subjectPROFESSIONAL STANDARDen
dc.subjectRUSSIAN FEDERATIONen
dc.subjectSECONDARY ANALYSISen
dc.subjectSTUDENTen
dc.titleCross-analysis of big data in accreditation of health specialistsen
dc.typeArticleen
dc.typeinfo:eu-repo/semantics/articleen
dc.typeinfo:eu-repo/semantics/publishedVersionen
dcterms.audienceOtheren
dcterms.audienceParents and Familiesen
dcterms.audienceResearchersen
dcterms.audienceSchool Support Staffen
dcterms.audienceStudentsen
dcterms.audienceTeachersen
local.issue5-
local.volume15-
local.identifier.doi10.29333/ejgm/93469-
local.identifier.scopus85056579992-
local.identifier.eid2-s2.0-85056579992-
local.identifier.affiliationFirst Moscow State Medical University named after I. M. Sechenov, Moscow, Russian Federationen
local.identifier.affiliationMinistry of Health, Moscow, Russian Federationen
local.identifier.affiliationFederal State Budget Scientific Institution “Institute for Strategy of Education”, Moscow, Russian Federationen
local.identifier.affiliationRussian State Vocational Pedagogical University, Ekaterinburg, Russian Federationen
local.identifier.affiliationIvanovo State University, Ivanovo, Russian Federationen
local.identifier.sourceScopusen
local.identifier.otherChelyshkova, M.B., First Moscow State Medical University named after I. M. Sechenov, Moscow, Russian Federationen
local.identifier.otherSemenova, T.V., Ministry of Health, Moscow, Russian Federationen
local.identifier.otherNaydenova, N.N., First Moscow State Medical University named after I. M. Sechenov, Moscow, Russian Federation, Federal State Budget Scientific Institution “Institute for Strategy of Education”, Moscow, Russian Federationen
local.identifier.otherDorozhkin, E.M., Russian State Vocational Pedagogical University, Ekaterinburg, Russian Federationen
local.identifier.otherMalygin, A.A., Ivanovo State University, Ivanovo, Russian Federationen
local.identifier.otherAkhunov, V.V., First Moscow State Medical University named after I. M. Sechenov, Moscow, Russian Federationen
local.identifier.otherWOS:000448396600001-
local.identifier.wos000448396600001-
local.description.orderem72-
Располагается в коллекциях:Научные публикации, проиндексированные в SCOPUS и WoS

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