Exam prediction and the general Practice Registrar Competency Assessment Grid (GPR-CAG)

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Abstract
In GP training, identifying early predictors of poor summative examination performance can be challenging. We aimed to establish whether external clinical teaching visit (ECTV) performance, measured using a validated instrument (GP Registrar Competency Assessment Grid, GPR-CAG) is predictive of Royal Australian College of General Practitioners (RACGP) Fellowship examination performance. A retrospective cohort study including GP registrars in New South Wales/Australian Capital Territory with ECTV data recorded during their first training term (GPT1), between 2014 and 2018, who attempted at least one Fellowship examination. Independent variables of interest included the four GPR-CAG factors assessed in GPT1 ('patient-centredness/caring', 'formulating hypotheses/management plans', 'professional responsibilities', 'physical examination skills'). Outcomes of interest included individual scores of the three summative examinations (Applied Knowledge Test (AKT); Key Feature Problem (KFP); and the Objective Structured Clinical Examination (OSCE)) and overall Pass/Fail status. Univariable and multivariable regression analyses were performed. Univariably, there were statistically significant associations (p < 0.01) between all four GPR-CAG factors and all four summative examination outcomes, except for 'formulating hypotheses/management plans' and OSCE score (p = 0.07). On multivariable analysis, each factor was significantly associated (p < 0.05) with at least one exam outcome, and 'physical examination skills' was significantly associated (p < 0.05) with all four exam outcomes. ECTV performance, via GPR-CAG scores, is predictive of RACGP Fellowship exam performance. The univariable findings highlight the pragmatic utility of ECTVs in flagging registrars who are at-risk of poor exam performance, facilitating early intervention. The multivariable associations of GPR-CAG scores and examination performance suggest that these scores provide predictive ability beyond that of other known predictors.
Funding Information
  • Department of Health

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