Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study
Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 3...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Violato, Claudio [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© International Association of Medical Science Educators 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Medical Science Educator - New York, NY [u.a.] : Springer, 2011, 26(2016), 3 vom: 01. Juni, Seite 317-322 |
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| Übergeordnetes Werk: |
volume:26 ; year:2016 ; number:3 ; day:01 ; month:06 ; pages:317-322 |
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| DOI / URN: |
10.1007/s40670-016-0263-6 |
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| Katalog-ID: |
SPR037944673 |
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| 245 | 1 | 0 | |a Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study |
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| 520 | |a Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. The results are evidence for the validity of Step 3 as a measure of clinical competence. | ||
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| 650 | 4 | |a Clinical reasoning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a USMLE Step3 |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Medical expertise |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Predictive validity |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Shen, E. |4 aut | |
| 700 | 1 | |a Gao, Hong |4 aut | |
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10.1007/s40670-016-0263-6 doi (DE-627)SPR037944673 (SPR)s40670-016-0263-6-e DE-627 ger DE-627 rakwb eng Violato, Claudio verfasserin aut Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Medical Science Educators 2016 Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. The results are evidence for the validity of Step 3 as a measure of clinical competence. Clinical competence (dpeaa)DE-He213 Clinical reasoning (dpeaa)DE-He213 USMLE Step3 (dpeaa)DE-He213 Medical expertise (dpeaa)DE-He213 Predictive validity (dpeaa)DE-He213 Shen, E. aut Gao, Hong aut Enthalten in Medical Science Educator New York, NY [u.a.] : Springer, 2011 26(2016), 3 vom: 01. Juni, Seite 317-322 (DE-627)815914563 (DE-600)2806660-1 2156-8650 nnns volume:26 year:2016 number:3 day:01 month:06 pages:317-322 https://dx.doi.org/10.1007/s40670-016-0263-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2016 3 01 06 317-322 |
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10.1007/s40670-016-0263-6 doi (DE-627)SPR037944673 (SPR)s40670-016-0263-6-e DE-627 ger DE-627 rakwb eng Violato, Claudio verfasserin aut Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Medical Science Educators 2016 Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. The results are evidence for the validity of Step 3 as a measure of clinical competence. Clinical competence (dpeaa)DE-He213 Clinical reasoning (dpeaa)DE-He213 USMLE Step3 (dpeaa)DE-He213 Medical expertise (dpeaa)DE-He213 Predictive validity (dpeaa)DE-He213 Shen, E. aut Gao, Hong aut Enthalten in Medical Science Educator New York, NY [u.a.] : Springer, 2011 26(2016), 3 vom: 01. Juni, Seite 317-322 (DE-627)815914563 (DE-600)2806660-1 2156-8650 nnns volume:26 year:2016 number:3 day:01 month:06 pages:317-322 https://dx.doi.org/10.1007/s40670-016-0263-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2016 3 01 06 317-322 |
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10.1007/s40670-016-0263-6 doi (DE-627)SPR037944673 (SPR)s40670-016-0263-6-e DE-627 ger DE-627 rakwb eng Violato, Claudio verfasserin aut Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Medical Science Educators 2016 Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. The results are evidence for the validity of Step 3 as a measure of clinical competence. Clinical competence (dpeaa)DE-He213 Clinical reasoning (dpeaa)DE-He213 USMLE Step3 (dpeaa)DE-He213 Medical expertise (dpeaa)DE-He213 Predictive validity (dpeaa)DE-He213 Shen, E. aut Gao, Hong aut Enthalten in Medical Science Educator New York, NY [u.a.] : Springer, 2011 26(2016), 3 vom: 01. Juni, Seite 317-322 (DE-627)815914563 (DE-600)2806660-1 2156-8650 nnns volume:26 year:2016 number:3 day:01 month:06 pages:317-322 https://dx.doi.org/10.1007/s40670-016-0263-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2016 3 01 06 317-322 |
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10.1007/s40670-016-0263-6 doi (DE-627)SPR037944673 (SPR)s40670-016-0263-6-e DE-627 ger DE-627 rakwb eng Violato, Claudio verfasserin aut Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Medical Science Educators 2016 Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. The results are evidence for the validity of Step 3 as a measure of clinical competence. Clinical competence (dpeaa)DE-He213 Clinical reasoning (dpeaa)DE-He213 USMLE Step3 (dpeaa)DE-He213 Medical expertise (dpeaa)DE-He213 Predictive validity (dpeaa)DE-He213 Shen, E. aut Gao, Hong aut Enthalten in Medical Science Educator New York, NY [u.a.] : Springer, 2011 26(2016), 3 vom: 01. Juni, Seite 317-322 (DE-627)815914563 (DE-600)2806660-1 2156-8650 nnns volume:26 year:2016 number:3 day:01 month:06 pages:317-322 https://dx.doi.org/10.1007/s40670-016-0263-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2016 3 01 06 317-322 |
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10.1007/s40670-016-0263-6 doi (DE-627)SPR037944673 (SPR)s40670-016-0263-6-e DE-627 ger DE-627 rakwb eng Violato, Claudio verfasserin aut Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © International Association of Medical Science Educators 2016 Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. The results are evidence for the validity of Step 3 as a measure of clinical competence. Clinical competence (dpeaa)DE-He213 Clinical reasoning (dpeaa)DE-He213 USMLE Step3 (dpeaa)DE-He213 Medical expertise (dpeaa)DE-He213 Predictive validity (dpeaa)DE-He213 Shen, E. aut Gao, Hong aut Enthalten in Medical Science Educator New York, NY [u.a.] : Springer, 2011 26(2016), 3 vom: 01. Juni, Seite 317-322 (DE-627)815914563 (DE-600)2806660-1 2156-8650 nnns volume:26 year:2016 number:3 day:01 month:06 pages:317-322 https://dx.doi.org/10.1007/s40670-016-0263-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 26 2016 3 01 06 317-322 |
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A Predictive Validity Study</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© International Association of Medical Science Educators 2016</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. 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Violato, Claudio |
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Violato, Claudio misc Clinical competence misc Clinical reasoning misc USMLE Step3 misc Medical expertise misc Predictive validity Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study |
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Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study Clinical competence (dpeaa)DE-He213 Clinical reasoning (dpeaa)DE-He213 USMLE Step3 (dpeaa)DE-He213 Medical expertise (dpeaa)DE-He213 Predictive validity (dpeaa)DE-He213 |
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does step 3 of the united states medical licensing exam measure clinical competence? a predictive validity study |
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Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study |
| abstract |
Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. The results are evidence for the validity of Step 3 as a measure of clinical competence. © International Association of Medical Science Educators 2016 |
| abstractGer |
Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. The results are evidence for the validity of Step 3 as a measure of clinical competence. © International Association of Medical Science Educators 2016 |
| abstract_unstemmed |
Purpose The main purpose of the present study was to evaluate the predictive validity of medical school admissions data, preclinical and clinical achievement data, postgraduate evaluations, and the United States Medical Licensing Exam Step 1 and 2 data for performance on Step 3. Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). Conclusions The predictive validity of Step 2, verbal reasoning, PGY1 assessment, Year 3 average for Step 3 performance is supported by the optimal regression model. Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies. The results are evidence for the validity of Step 3 as a measure of clinical competence. © International Association of Medical Science Educators 2016 |
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Does Step 3 of the United States Medical Licensing Exam Measure Clinical Competence? A Predictive Validity Study |
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Shen, E. Gao, Hong |
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Methods A total of 321 physicians (178 men, 55.4 %; 143 women, 44.6 %) participated. Admissions data, medical school preclinical achievement data, medical school clerkship clinical performance data, postgraduate clinical performance data, and USMLE Step 1 and 2 data were independent variables. Step 3 was the dependent variable in a backwards multiple regression. An analysis of covariance with Step 3 scores as the dependent variable, Step 2 scores as the covariate, and residency program (narrowly focused vs. broadly focused two levels) as the independent variable was conducted. Results The regression analyses resulted in an optimal model fit with the most parsimonious combination of independent variables (multiple R = 0.686, R2 = 0.470, p < 0.001): Step 2 (β = 0.606) + verbal reasoning (β = 0.125) + PGY1 (β = 0.161) + Year 3 (β = 0.157). Candidates in broadly focused residencies scored higher on Step 3 compared to those in narrowly focused residencies (F = 5.17, p < 0.05). 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