VP64 Post-Graduation Selection Using Multi-Criteria Decision Analysis

dc.contributor.authorSalgado, Jessica Baldissara
dc.contributor.authorSantos, Marisa da Silva
dc.contributor.authorSenna, Katia Marie Simões e
dc.contributor.authorMagliano, Carlos
dc.date.accessioned2022-09-16T13:23:12Z
dc.date.available2022-09-16T13:23:12Z
dc.date.issued2017
dc.description.abstractINTRODUCTION: Selecting candidates for graduate programs is considered to be a complex task, often subject to failures, especially regarding to the appraisal of non-cognitive (1,2) skills (for example, Motivation). Identifying suitable candidates is important for the overall success of the graduate programs, since dropouts and low productivity negatively affect the program classification by the Brazilian Governmental Agency. This study aims to describe the use of Multicriteria Decision Analysis (3) in the selection of candidates for a master degree program in Health Technology Assessment (HTA). METHODS: The Multicriteria Decision Analysis (MCDA) technique was used to measure value in the selection of students applying for a masters degree program, in 2017, using Multi-Attribute Value Theory methods (MAVT) method. The examiners group consisting of full-time professors who selected the criteria, blinded ranked and assigned weight relative to each criterion, using swing weights technique, normalized to 100 percent. During the face to face interview with the students, each evaluator professor filled an individual spreadsheet based on pre-defined questions and curriculum analysis. The results were summarized with a mean. For criterion performance, a value from 0 until 3 was assigned if the candidate didn't meet the criterion, partially meet and fully meet. The performance scores were multiplied by the weight of each criterion, the results were summarized by simple additive model, and the candidates were ranked. RESULTS: An interview was conducted with the examining group evaluating MCDA asking for difficulties, time consumed and if the result was considered fair. Seven criteria were listed: “Comprehension of HTA”, “Motivation”, “Ability to disseminate information”, “Availability to attend the course”, “Scientific production”, “Potential to work in HTA area” and “Scientific writing skills”. The highest weight (24 percent) was attributed to the “Potential to work in HTA area” and “Scientific writing skills” (20 percent). The evaluating group was unanimous in considering the process easy, fast and fair. CONCLUSIONS: The MCDA technique was applied successfully in student selection. Further prospective studies are needed.en
dc.identifier.citationSantos M, Senna K, Magliano C, Baldissara J. VP64 Post-Graduation Selection Using Multi-Criteria Decision Analysis. International Journal of Technology Assessment in Health Care. Cambridge University Press; 2017;33(S1):178–9. Doi: 10.1017/S0266462317003415.
dc.identifier.otherDOI: 10.1017/S0266462317003415
dc.identifier.urihttps://dspace.inc.saude.gov.br/handle/123456789/293
dc.language.isoen
dc.publisherInternational Journal of Technology Assessment in Health Care
dc.subjectTechnology Assessment, Biomedicalen
dc.subjectAvaliação de Tecnologias em Saúdept
dc.titleVP64 Post-Graduation Selection Using Multi-Criteria Decision Analysisen
dc.typePresentation
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