Health Care Education
Jill A. Turner [bio]
(313) 494-6903 / (313) 993-1157
Health Care Education Subject Resources
Monitoring and regulation of learning in medical education: the need for predictive cues
Context Being able to accurately monitor learning activities is a key element in self-regulated learning in all settings, including medical schools. Yet students’ ability to monitor their progress is often limited, leading to inefficient use of study time. Interventions that improve the accuracy of students’ monitoring can optimise self-regulated learning, leading to higher achievement. This paper reviews findings from cognitive psychology and explores potential applications in medical education, as well as areas for future research. Cognitive Psychology Effective monitoring depends on students’ ability to generate information (‘cues’) that accurately reflects their knowledge and skills. The ability of these ‘cues’ to predict achievement is referred to as ‘cue diagnosticity’. Interventions that improve the ability of students to elicit predictive cues typically fall into two categories: (i) self-generation of cues and (ii) generation of cues that is delayed after self-study. Providing feedback and support is useful when cues are predictive but may be too complex to be readily used. Application to Medical Education Limited evidence exists about interventions to improve the accuracy of self-monitoring among medical students or trainees. Developing interventions that foster use of predictive cues can enhance the accuracy of self-monitoring, thereby improving self-study and clinical reasoning. First, insight should be gained into the characteristics of predictive cues used by medical students and trainees. Next, predictive cue prompts should be designed and tested to improve monitoring and regulation of learning. Finally, the use of predictive cues should be explored in relation to teaching and learning clinical reasoning. Conclusions Improving self-regulated learning is important to help medical students and trainees efficiently acquire knowledge and skills necessary for clinical practice. Interventions that help students generate and use predictive cues hold the promise of improved self-regulated learning and achievement. This framework is applicable to learning in several areas, including the development of clinical reasoning.
When I say … dual-processing theory
The economic cost of failure in clinical education: a multi-perspective analysis
Context Failure by students in health professional clinical education intertwines the health and education sectors, with actions in one having potential downstream effects on the other. It is unknown what economic costs are associated with failure, how these costs are distributed, and the impacts these have on students, clinicians and workplace productivity. An understanding of cost drivers and cost boundaries will enable evidence-based targeting of strategic investments into clinical education, including where they should be made and by whom. Objectives This study was designed to determine the additional economic costs associated with failure by students in health professional clinical education. Methods A cost analysis study involving cost identification, measurement, valuation and the calculation of total cost was conducted. Costs were considered from the perspective of the student, the education institution, the clinical educator, the health service placement provider organisation and the government. Data were based on a 5-week clinical education programme at Monash University, Australia. Data were collected using quantitative surveys and interviews conducted with health professional students, clinical educators and education institute staff. Reference group representation was also sought at various education institution and health service organisation levels. A transferable model with sensitivity analysis was developed. Results There is a total additional cost of US$9371 per student failing in clinical education from the perspective of all stakeholders considered. Students bear the majority of this burden, incurring 49% of costs, followed by the government (22%), the education institution (18%), the health service organisation (10%) and the clinical educator (1%). Conclusions Strong economic links for multiple stakeholders as a result of failure by students in clinical education have been identified. The cost burden is skewed in the direction of students. Any generalisation of these results should be made with consideration for the unique clinical education context in which each health professional education programme operates.
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