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• Titulo Artículo: Mind the Gap: The Prospects of Missing Data
• Resumen: Competency-based medical education (CBME) is changing the way physicians are educated,1–4 with a heavy emphasis on quantifying and qualifying their performance via robust assessments. The central tenet of CBME is that trainees must demonstrate competence in applying acquired skills during patient care activities,5 and CBME requires assessment strategies that ensure trainees apply their knowledge, skills, and abilities in authentic or simulated environments.6 This necessity for direct observation and assessment of trainees’ performance has resulted in a shift toward workplace-based assessments (WBAs) as a primary method of assessment. There is no single comprehensive WBA assessment tool, and experts have argued that decisions regarding trainees’ progression should be based on aggregates of multiple measures of performance using both qualitative and quantitative methods.6,7 The implementation of WBAs allows educators to identify patterns in the development of knowledge, skills, and performance. While educators and researchers have paid considerable attention to understanding patterns in WBA data, less attention has been paid to missing data. Identifying and understanding potential patterns underlying missing data is an important step in accurately interpreting WBA data. While mechanisms exist to deal with missing data (eg, multiple imputation and maximum likelihood methods), many of these presume that data are missing at random.8 This may not be the case in the context of WBA portfolios. For example, residents may be more likely to complete WBAs for tasks that they enjoy and/or perform well; consequently, there may be missing data for more poorly developed knowledge, skills, and abilities. Similarly, certain WBA tools (eg, multi-source feedback) may be particularly challenging to complete because of the logistics of collecting the data. Nonrandomly missing data could threaten the inherent validity of WBA portfolios. The purpose of this study is twofold. First, we examined whether data are, in fact, missing at random across various competencies within the context of our local WBA system. Second, we assessed whether the amount of missing data correlated with overall resident performance as determined by a panel of faculty from the residency education committee
• Autores: Jonathan Sherbino, Teresa M. Chan, Meghan McConnell, .
• Titulo Revista: Journal of Graduate Medical Education, .
• Numero: 5
• Volumen:8
• Fecha de publicación: 2016.
• Base de Datos Bibliográfica: Medline-PubMed , .
• Suplemento:
• Idioma: Inglés
• Página Inicial: 708 Página Final: 712
• ISBN: 1949-8357

• Título Medline-PubMed : Mind the Gap: The Prospects of Missing Data
• Resumen: Competency-based medical education (CBME) is changing the way physicians are educated,1–4 with a heavy emphasis on quantifying and qualifying their performance via robust assessments. The central tenet of CBME is that trainees must demonstrate competence in applying acquired skills during patient care activities,5 and CBME requires assessment strategies that ensure trainees apply their knowledge, skills, and abilities in authentic or simulated environments.6 This necessity for direct observation and assessment of trainees’ performance has resulted in a shift toward workplace-based assessments (WBAs) as a primary method of assessment. There is no single comprehensive WBA assessment tool, and experts have argued that decisions regarding trainees’ progression should be based on aggregates of multiple measures of performance using both qualitative and quantitative methods.6,7 The implementation of WBAs allows educators to identify patterns in the development of knowledge, skills, and performance. While educators and researchers have paid considerable attention to understanding patterns in WBA data, less attention has been paid to missing data. Identifying and understanding potential patterns underlying missing data is an important step in accurately interpreting WBA data. While mechanisms exist to deal with missing data (eg, multiple imputation and maximum likelihood methods), many of these presume that data are missing at random.8 This may not be the case in the context of WBA portfolios. For example, residents may be more likely to complete WBAs for tasks that they enjoy and/or perform well; consequently, there may be missing data for more poorly developed knowledge, skills, and abilities. Similarly, certain WBA tools (eg, multi-source feedback) may be particularly challenging to complete because of the logistics of collecting the data. Nonrandomly missing data could threaten the inherent validity of WBA portfolios. The purpose of this study is twofold. First, we examined whether data are, in fact, missing at random across various competencies within the context of our local WBA system. Second, we assessed whether the amount of missing data correlated with overall resident performance as determined by a panel of faculty from the residency education committee
• Autores : Jonathan Sherbino; Teresa M. Chan; Meghan McConnell;
• Autor corporativo: Journal of Graduate Medical Education,
• Fecha de publicación: 2016.
• Tipo : Medline-PubMed .
• Idioma: Inglés

• Título Medline-PubMed : Mind the Gap: The Prospects of Missing Data
• Resumen: Competency-based medical education (CBME) is changing the way physicians are educated,1–4 with a heavy emphasis on quantifying and qualifying their performance via robust assessments. The central tenet of CBME is that trainees must demonstrate competence in applying acquired skills during patient care activities,5 and CBME requires assessment strategies that ensure trainees apply their knowledge, skills, and abilities in authentic or simulated environments.6 This necessity for direct observation and assessment of trainees’ performance has resulted in a shift toward workplace-based assessments (WBAs) as a primary method of assessment. There is no single comprehensive WBA assessment tool, and experts have argued that decisions regarding trainees’ progression should be based on aggregates of multiple measures of performance using both qualitative and quantitative methods.6,7 The implementation of WBAs allows educators to identify patterns in the development of knowledge, skills, and performance. While educators and researchers have paid considerable attention to understanding patterns in WBA data, less attention has been paid to missing data. Identifying and understanding potential patterns underlying missing data is an important step in accurately interpreting WBA data. While mechanisms exist to deal with missing data (eg, multiple imputation and maximum likelihood methods), many of these presume that data are missing at random.8 This may not be the case in the context of WBA portfolios. For example, residents may be more likely to complete WBAs for tasks that they enjoy and/or perform well; consequently, there may be missing data for more poorly developed knowledge, skills, and abilities. Similarly, certain WBA tools (eg, multi-source feedback) may be particularly challenging to complete because of the logistics of collecting the data. Nonrandomly missing data could threaten the inherent validity of WBA portfolios. The purpose of this study is twofold. First, we examined whether data are, in fact, missing at random across various competencies within the context of our local WBA system. Second, we assessed whether the amount of missing data correlated with overall resident performance as determined by a panel of faculty from the residency education committee
• Autores : Jonathan Sherbino; Teresa M. Chan; Meghan McConnell;
• Autor corporativo: Journal of Graduate Medical Education,
• Fecha de publicación: 2016.
• Paginas: 708.
• ISBN: 1949-8357.
• Idioma: Inglés

• Titulo Artículo: Mind the Gap: The Prospects of Missing Data
• Resumen: Competency-based medical education (CBME) is changing the way physicians are educated,1–4 with a heavy emphasis on quantifying and qualifying their performance via robust assessments. The central tenet of CBME is that trainees must demonstrate competence in applying acquired skills during patient care activities,5 and CBME requires assessment strategies that ensure trainees apply their knowledge, skills, and abilities in authentic or simulated environments.6 This necessity for direct observation and assessment of trainees’ performance has resulted in a shift toward workplace-based assessments (WBAs) as a primary method of assessment. There is no single comprehensive WBA assessment tool, and experts have argued that decisions regarding trainees’ progression should be based on aggregates of multiple measures of performance using both qualitative and quantitative methods.6,7 The implementation of WBAs allows educators to identify patterns in the development of knowledge, skills, and performance. While educators and researchers have paid considerable attention to understanding patterns in WBA data, less attention has been paid to missing data. Identifying and understanding potential patterns underlying missing data is an important step in accurately interpreting WBA data. While mechanisms exist to deal with missing data (eg, multiple imputation and maximum likelihood methods), many of these presume that data are missing at random.8 This may not be the case in the context of WBA portfolios. For example, residents may be more likely to complete WBAs for tasks that they enjoy and/or perform well; consequently, there may be missing data for more poorly developed knowledge, skills, and abilities. Similarly, certain WBA tools (eg, multi-source feedback) may be particularly challenging to complete because of the logistics of collecting the data. Nonrandomly missing data could threaten the inherent validity of WBA portfolios. The purpose of this study is twofold. First, we examined whether data are, in fact, missing at random across various competencies within the context of our local WBA system. Second, we assessed whether the amount of missing data correlated with overall resident performance as determined by a panel of faculty from the residency education committee
• Fecha de publicación: 2016.
• Autor corporativo: Journal of Graduate Medical Education, .
• Idioma: Inglés

Palabras claves:

• Competency-based medical education
• WBAs
• Resident
• Medical education

Público objetivo:

• Docentes
• Medicos
• Investigadores
• Educadores Medicos
• Otros profesionales de la salud