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Titulo Artículo:
Nuance and Noise: Lessons Learned From Longitudinal Aggregated Assessment Data.
Resumen:
BACKGROUND: Competency-based medical education requires frequent assessment to tailor learning experiences to the needs of trainees. In 2012, we implemented the McMaster Modular Assessment Program, which captures shift-based assessments of resident global performance. OBJECTIVE: We described patterns (ie, trends and sources of variance) in aggregated workplace-based assessment data. METHODS: Emergency medicine residents and faculty members from 3 Canadian university-affiliated, urban, tertiary care teaching hospitals participated in this study. During each shift, supervising physicians rated residents' performance using a behaviorally anchored scale that hinged on endorsements for progression. We used a multilevel regression model to examine the relationship between global rating scores and time, adjusting for data clustering by resident and rater. RESULTS: We analyzed data from 23 second-year residents between July 2012 and June 2015, which yielded 1498 unique ratings (65 ± 18.5 per resident) from 82 raters. The model estimated an average score of 5.7 ± 0.6 at baseline, with an increase of 0.005 ± 0.01 for each additional assessment. There was significant variation among residents' starting score (y-intercept) and trajectory (slope). CONCLUSIONS: Our model suggests that residents begin at different points and progress at different rates. Meta-raters such as program directors and Clinical Competency Committee members should bear in mind that progression may take time and learning trajectories will be nuanced. Individuals involved in ratings should be aware of sources of noise in the system, including the raters themselves.
Fecha de publicación:
2017.
Autores :
Sherbino, Jonathan;
Mercuri, Mathew;
Chan, Teresa M;
Autor corporativo:
Journal of graduate medical education,
Editores:
Biblioteca Virtual en Salud(BVS) ;
Signatura Topográfica:
6
Idioma:
Inglés
Páginas:
724
ISBN:
1949-8357
Existencias:
729
Palabras claves:
Emergency Medicine
Medical Education
Workplace
Público objetivo:
Posgrado
Docentes
Medicos
Educadores Medicos
Titulo Artículo:
Nuance and Noise: Lessons Learned From Longitudinal Aggregated Assessment Data.
Resumen:
BACKGROUND: Competency-based medical education requires frequent assessment to tailor learning experiences to the needs of trainees. In 2012, we implemented the McMaster Modular Assessment Program, which captures shift-based assessments of resident global performance. OBJECTIVE: We described patterns (ie, trends and sources of variance) in aggregated workplace-based assessment data. METHODS: Emergency medicine residents and faculty members from 3 Canadian university-affiliated, urban, tertiary care teaching hospitals participated in this study. During each shift, supervising physicians rated residents' performance using a behaviorally anchored scale that hinged on endorsements for progression. We used a multilevel regression model to examine the relationship between global rating scores and time, adjusting for data clustering by resident and rater. RESULTS: We analyzed data from 23 second-year residents between July 2012 and June 2015, which yielded 1498 unique ratings (65 ± 18.5 per resident) from 82 raters. The model estimated an average score of 5.7 ± 0.6 at baseline, with an increase of 0.005 ± 0.01 for each additional assessment. There was significant variation among residents' starting score (y-intercept) and trajectory (slope). CONCLUSIONS: Our model suggests that residents begin at different points and progress at different rates. Meta-raters such as program directors and Clinical Competency Committee members should bear in mind that progression may take time and learning trajectories will be nuanced. Individuals involved in ratings should be aware of sources of noise in the system, including the raters themselves.
Fecha de publicación:
2017.
Autores :
Sherbino, Jonathan;
Mercuri, Mathew;
Chan, Teresa M;
Autor corporativo:
Journal of graduate medical education,
Editores:
Biblioteca Virtual en Salud(BVS) ;
Signatura Topográfica:
6
Idioma:
Inglés
Páginas:
724
Existencias:
729
Palabras claves:
Emergency Medicine
Medical Education
Workplace
Público objetivo:
Posgrado
Docentes
Medicos
Educadores Medicos
Titulo Artículo:
Nuance and Noise: Lessons Learned From Longitudinal Aggregated Assessment Data.
Resumen:
BACKGROUND: Competency-based medical education requires frequent assessment to tailor learning experiences to the needs of trainees. In 2012, we implemented the McMaster Modular Assessment Program, which captures shift-based assessments of resident global performance. OBJECTIVE: We described patterns (ie, trends and sources of variance) in aggregated workplace-based assessment data. METHODS: Emergency medicine residents and faculty members from 3 Canadian university-affiliated, urban, tertiary care teaching hospitals participated in this study. During each shift, supervising physicians rated residents' performance using a behaviorally anchored scale that hinged on endorsements for progression. We used a multilevel regression model to examine the relationship between global rating scores and time, adjusting for data clustering by resident and rater. RESULTS: We analyzed data from 23 second-year residents between July 2012 and June 2015, which yielded 1498 unique ratings (65 ± 18.5 per resident) from 82 raters. The model estimated an average score of 5.7 ± 0.6 at baseline, with an increase of 0.005 ± 0.01 for each additional assessment. There was significant variation among residents' starting score (y-intercept) and trajectory (slope). CONCLUSIONS: Our model suggests that residents begin at different points and progress at different rates. Meta-raters such as program directors and Clinical Competency Committee members should bear in mind that progression may take time and learning trajectories will be nuanced. Individuals involved in ratings should be aware of sources of noise in the system, including the raters themselves.
Autores:
Sherbino, Jonathan
,
Mercuri, Mathew
,
Chan, Teresa M
,
.
Titulo Revista:
Journal of graduate medical education,
.
Numero:
6
Volumen:
9
Fecha de publicación:
2017.
Base de Datos Bibliográfica:
Biblioteca Virtual en Salud(BVS) ,
.
Suplemento:
Idioma:
Inglés
Página Inicial:
724
Página Final:
729
ISBN:
1949-8357
Palabras claves:
Emergency Medicine
Medical Education
Workplace
Público objetivo:
Posgrado
Docentes
Medicos
Educadores Medicos
Título Biblioteca Virtual en Salud(BVS) :
Nuance and Noise: Lessons Learned From Longitudinal Aggregated Assessment Data.
Resumen:
BACKGROUND: Competency-based medical education requires frequent assessment to tailor learning experiences to the needs of trainees. In 2012, we implemented the McMaster Modular Assessment Program, which captures shift-based assessments of resident global performance. OBJECTIVE: We described patterns (ie, trends and sources of variance) in aggregated workplace-based assessment data. METHODS: Emergency medicine residents and faculty members from 3 Canadian university-affiliated, urban, tertiary care teaching hospitals participated in this study. During each shift, supervising physicians rated residents' performance using a behaviorally anchored scale that hinged on endorsements for progression. We used a multilevel regression model to examine the relationship between global rating scores and time, adjusting for data clustering by resident and rater. RESULTS: We analyzed data from 23 second-year residents between July 2012 and June 2015, which yielded 1498 unique ratings (65 ± 18.5 per resident) from 82 raters. The model estimated an average score of 5.7 ± 0.6 at baseline, with an increase of 0.005 ± 0.01 for each additional assessment. There was significant variation among residents' starting score (y-intercept) and trajectory (slope). CONCLUSIONS: Our model suggests that residents begin at different points and progress at different rates. Meta-raters such as program directors and Clinical Competency Committee members should bear in mind that progression may take time and learning trajectories will be nuanced. Individuals involved in ratings should be aware of sources of noise in the system, including the raters themselves.
Autores :
Sherbino, Jonathan;
Mercuri, Mathew;
Chan, Teresa M;
Autor corporativo:
Journal of graduate medical education,
Fecha de publicación:
2017.
Tipo :
Biblioteca Virtual en Salud(BVS) .
Idioma:
Inglés
Palabras claves:
Emergency Medicine
Medical Education
Workplace
Público objetivo:
Posgrado
Docentes
Medicos
Educadores Medicos
Título Biblioteca Virtual en Salud(BVS) :
Nuance and Noise: Lessons Learned From Longitudinal Aggregated Assessment Data.
Resumen:
BACKGROUND: Competency-based medical education requires frequent assessment to tailor learning experiences to the needs of trainees. In 2012, we implemented the McMaster Modular Assessment Program, which captures shift-based assessments of resident global performance. OBJECTIVE: We described patterns (ie, trends and sources of variance) in aggregated workplace-based assessment data. METHODS: Emergency medicine residents and faculty members from 3 Canadian university-affiliated, urban, tertiary care teaching hospitals participated in this study. During each shift, supervising physicians rated residents' performance using a behaviorally anchored scale that hinged on endorsements for progression. We used a multilevel regression model to examine the relationship between global rating scores and time, adjusting for data clustering by resident and rater. RESULTS: We analyzed data from 23 second-year residents between July 2012 and June 2015, which yielded 1498 unique ratings (65 ± 18.5 per resident) from 82 raters. The model estimated an average score of 5.7 ± 0.6 at baseline, with an increase of 0.005 ± 0.01 for each additional assessment. There was significant variation among residents' starting score (y-intercept) and trajectory (slope). CONCLUSIONS: Our model suggests that residents begin at different points and progress at different rates. Meta-raters such as program directors and Clinical Competency Committee members should bear in mind that progression may take time and learning trajectories will be nuanced. Individuals involved in ratings should be aware of sources of noise in the system, including the raters themselves.
Autores :
Sherbino, Jonathan;
Mercuri, Mathew;
Chan, Teresa M;
Autor corporativo:
Journal of graduate medical education,
Fecha de publicación:
2017.
Paginas:
724.
ISBN:
1949-8357.
Idioma:
Inglés
Palabras claves:
Emergency Medicine
Medical Education
Workplace
Público objetivo:
Posgrado
Docentes
Medicos
Educadores Medicos
Titulo Artículo:
Nuance and Noise: Lessons Learned From Longitudinal Aggregated Assessment Data.
Resumen:
BACKGROUND: Competency-based medical education requires frequent assessment to tailor learning experiences to the needs of trainees. In 2012, we implemented the McMaster Modular Assessment Program, which captures shift-based assessments of resident global performance. OBJECTIVE: We described patterns (ie, trends and sources of variance) in aggregated workplace-based assessment data. METHODS: Emergency medicine residents and faculty members from 3 Canadian university-affiliated, urban, tertiary care teaching hospitals participated in this study. During each shift, supervising physicians rated residents' performance using a behaviorally anchored scale that hinged on endorsements for progression. We used a multilevel regression model to examine the relationship between global rating scores and time, adjusting for data clustering by resident and rater. RESULTS: We analyzed data from 23 second-year residents between July 2012 and June 2015, which yielded 1498 unique ratings (65 ± 18.5 per resident) from 82 raters. The model estimated an average score of 5.7 ± 0.6 at baseline, with an increase of 0.005 ± 0.01 for each additional assessment. There was significant variation among residents' starting score (y-intercept) and trajectory (slope). CONCLUSIONS: Our model suggests that residents begin at different points and progress at different rates. Meta-raters such as program directors and Clinical Competency Committee members should bear in mind that progression may take time and learning trajectories will be nuanced. Individuals involved in ratings should be aware of sources of noise in the system, including the raters themselves.
Fecha de publicación:
2017.
Autor corporativo:
Journal of graduate medical education,
.
Idioma:
Inglés
Palabras claves:
Emergency Medicine
Medical Education
Workplace
Público objetivo:
Posgrado
Docentes
Medicos
Educadores Medicos
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Hola, encontré este documento en la biblioteca especializada en Educación Médica de ASCOFAME :Sherbino, Jonathan; Nuance and Noise: Lessons Learned From Longitudinal Aggregated Assessment Data.(2017). Podras consultarlo en el Siguiente link: https://ascofame.org.co/biblioteca/detalle_documento.php?id=2284
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Sherbino, Jonathan Mercuri, Mathew Chan, Teresa M Sherbino, Jonathan Mercuri, Mathew Chan, Teresa M Nuance and Noise: Lessons Learned From Longitudinal Aggregated Assessment Data.. 2017; 9Ed. 724.
