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Value of supervised learning events in predicting doctors in difficulty
Author(s) -
Patel Mumtaz,
Agius Steven,
Wilkinson Jack,
Patel Leena,
Baker Paul
Publication year - 2016
Publication title -
medical education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.776
H-Index - 138
eISSN - 1365-2923
pISSN - 0308-0110
DOI - 10.1111/medu.12996
Subject(s) - value (mathematics) , psychology , medicine , medical education , computer science , machine learning
Context In the UK , supervised learning events ( SLE ) replaced traditional workplace‐based assessments for foundation‐year trainees in 2012. A key element of SLE s was to incorporate trainee reflection and assessor feedback in order to drive learning and identify training issues early. Few studies, however, have investigated the value of SLE s in predicting doctors in difficulty. This study aimed to identify principles that would inform understanding about how and why SLE s work or not in identifying doctors in difficulty (DiD). Methods A retrospective case‒control study of North West Foundation School trainees’ electronic portfolios was conducted. Cases comprised all known DiD. Controls were randomly selected from the same cohort. Free‐text supervisor comments from each SLE were assessed for the four domains defined in the General Medical Council's Good Medical Practice Guidelines and each scored blindly for level of concern using a three‐point ordinal scale. Cumulative scores for each SLE were then analysed quantitatively for their predictive value of actual DiD. A qualitative thematic analysis was also conducted. Results The prevalence of DiD in this sample was 6.5%. Receiver operator characteristic curve analysis showed that Team Assessment of Behaviour ( TAB ) was the only SLE strongly predictive of actual DiD status. The Educational Supervisor Report ( ESR ) was also strongly predictive of DiD status. Fisher's test showed significant associations of TAB and ESR for both predicted and actual DiD status and also the health and performance subtypes. None of the other SLE s showed significant associations. Qualitative data analysis revealed inadequate completion and lack of constructive, particularly negative, feedback. This indicated that SLE s were not used to their full potential. Conclusions TAB and the ESR are strongly predictive of DiD. However, SLE s are not being used to their full potential, and the quality of completion of reports on SLE s and feedback needs to be improved in order to better identify and manage DiD.