Abstract
Background: In 2017 Bristol Medical School adopted a peer-led teaching approach to deliver Basic Life Support training to 1st year medical students: delivered over 3 weeks. It can be challenging to identify and record which candidates are struggling with their learning so that remedial support can be offered. We have developed a novel online scoring system to better track candidate progress, modelled on early warning score systems used in clinical practice.
Summary of Work: During training, instructors rate candidate performance at six time-points (1= inadequate demonstration of skills, 10= Perfect demonstration of skills), via an excel spreadsheet stored in a secure university shared drive. By using conditional formatting, the output display gives a real time visual indication of performance for the session lead. To improve reliability of the conditional formatting display, a repeated-measures ANOVA was performed on the scores using the analysis software SPSS and trends analysed. A binary variable was then used, classifying candidates as above or below the Mean(x̄)-1SD. A sub-analysis was performed to identify significant differences within this binary classification. Values are presented as x̄±SD unless otherwise stated.
Summary of Results: A significant linear trend was demonstrated (P<0.01) for the progression of candidates over the 3 weeks. The average session score increases from 4.47±1.67 at the start to 8.33±1.85 before the final assessment. The largest gain is seen between session 1 and 2 with a difference of 1.71±1.12.
Discussion and Conclusion: By examining the trend over the time, we have identified candidates requiring more support with greater ease: either singly scoring less than 1SD of the mean or the rate of improvement between sessions is below x̄-1SD. We have also used our statistical analysis to improve the conditional formatting of the real time output display. By identifying these struggling candidates early and effectively we are able to offer the necessarily remedial support to ensure the development of their skills for both their assessment and for safe practice in their future careers.
Take Home Message: The use of live scoring system with conditional formatting for a skills-based training course enables quick and effective identification of candidates needing support.
Summary of Work: During training, instructors rate candidate performance at six time-points (1= inadequate demonstration of skills, 10= Perfect demonstration of skills), via an excel spreadsheet stored in a secure university shared drive. By using conditional formatting, the output display gives a real time visual indication of performance for the session lead. To improve reliability of the conditional formatting display, a repeated-measures ANOVA was performed on the scores using the analysis software SPSS and trends analysed. A binary variable was then used, classifying candidates as above or below the Mean(x̄)-1SD. A sub-analysis was performed to identify significant differences within this binary classification. Values are presented as x̄±SD unless otherwise stated.
Summary of Results: A significant linear trend was demonstrated (P<0.01) for the progression of candidates over the 3 weeks. The average session score increases from 4.47±1.67 at the start to 8.33±1.85 before the final assessment. The largest gain is seen between session 1 and 2 with a difference of 1.71±1.12.
Discussion and Conclusion: By examining the trend over the time, we have identified candidates requiring more support with greater ease: either singly scoring less than 1SD of the mean or the rate of improvement between sessions is below x̄-1SD. We have also used our statistical analysis to improve the conditional formatting of the real time output display. By identifying these struggling candidates early and effectively we are able to offer the necessarily remedial support to ensure the development of their skills for both their assessment and for safe practice in their future careers.
Take Home Message: The use of live scoring system with conditional formatting for a skills-based training course enables quick and effective identification of candidates needing support.
| Original language | English |
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| Publication status | Published - 7 Sept 2020 |
| Event | AMEE 2020: The Virtual Conference - Virtual Duration: 7 Sept 2020 → 9 Sept 2020 https://amee.org/conferences/amee-2020 |
Conference
| Conference | AMEE 2020: The Virtual Conference |
|---|---|
| Period | 7/09/20 → 9/09/20 |
| Internet address |