Risk factors associated with academic difficulty in an Australian regionally located medical school

Malau‐Aduli, Bunmi S.; O’Connor, Teresa; Ray, Robin; Kruk, Yolanda van der; Bellingan, Michelle; Teague, Peta-Ann

doi:10.1186/s12909-017-1095-9

Cited by 17 publications

(16 citation statements)

References 43 publications

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“…In spite of this effective academic support system, about 16% of the students in the JCU MBBS course experience academic difficulty, while 3% of these students fail and exit the program [12]. Therefore, there is a need to understand the characteristics and functioning of the failing medical students to better support them.…”

Section: Introductionmentioning

confidence: 99%

Dealing with Academic Difficulty in Medical School: A Pilot Study

et al. 2020

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Some students struggle through medical school and do not have the confidence to seek help. This pilot study sought to explore the challenges and needs of medical students experiencing academic difficulty. Semi-structured interviews and online surveys were used to collect data from an academic advisor and thirteen medical students who had experienced academic difficulty. Unexpected academic failure and the loss of self-efficacy contributed to students hiding their academic difficulty and avoiding available support systems. Despite the sampling limitations, the findings of this pilot study have value in giving direction to future research. Programs that will change the current attitudes to academic difficulty, normalising access to support and encouraging early intervention, are needed to build the capacity for excellence among these students.

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Section: Introductionmentioning

confidence: 99%

Dealing with Academic Difficulty in Medical School: A Pilot Study

et al. 2020

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“…A prior systematic review reported several factors that could affect medical students' academic success, including previous academic performance, and cognitive ability [3]. Studies have also reported associations between demographic factors, such as gender and age, and students' academic performance and/or dropout rate [4][5][6][7][8][9][10]. Furthermore, contextual factors such as type of school (e.g., state-funded schools vs. independent schools) [11] and hometown (rural or remote vs. metropolitan areas) [4,12] have been linked to student academic performance.…”

Section: Introductionmentioning

confidence: 99%

Associations between demographic factors and the academic trajectories of medical students in Japan

et al. 2020

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BackgroundGroup-based trajectory modeling is a useful tool for categorizing students' academic trajectories and their determinants. Using insights gained from the analyses, we can identify students at risk for poor academic performance and monitor them to provide support. To date, studies investigating the associations between demographic factors and academic performance trajectories among medical students are scarce. The study objective was to examine the associations between demographic factors and academic performance trajectories in medical students using group-based trajectory modeling. MethodsParticipants included all medical students admitted to Tokyo Medical and Dental University in Japan in 2013 and 2014 (n = 202). Academic performance was evaluated by biannual grade point average (GPA) scores in preclinical years. We used group-based trajectory modeling to categorize students into GPA trajectories. Multinomial logistic regression was used to examine the association between the odds of being in a certain GPA trajectory group and demographic factors such as high school type, high school geographical area, admission test type, high school graduation year, whether the student was a biology major, and sex.

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“…It is unclear why medical students who belonged to a neighborhood HS have better chance of passing the NMLE, and we found no such study on the relationship between NMLE and the location of HS or hometown. Given previous study on academic performance [ 16 , 18 ], students from the neighborhood city/town might be able to receive various kinds of supports from their families physically, economically, and psychologically. Further investigations are required.…”

Section: Discussionmentioning

confidence: 99%

“…However, these studies did not confirm their results using predictors in a new group of students. Previous studies about medical students’ academic success reported academic performance associated with not only post admission variables such as previous academic performance [ 15 , 16 ] and Objective Structured Clinical Examination (OSCE) [ 17 ], but also pre-admission variables such as gender [ 17 ], age at admission [ 16 ], hometown [ 16 , 18 ], type of high school (HS) [ 19 ], HS grade point average (GPA) [ 20 ], and entrance exam for medical schools [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate

2020

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Background Students who fail to pass the National Medical Licensure Examination (NMLE) pose a huge problem from the educational standpoint of healthcare professionals. In the present study, we developed a formula of predictive pass rate (PPR)” which reliably predicts medical students who will fail the NMLE in Japan, and provides an adequate academic support for them. Methods Six consecutive cohorts of 531 medical students between 2012 and 2017, Gifu University Graduate School of Medicine, were investigated. Using 7 variables before the admission to medical school and 10 variables after admission, we developed a prediction formula to obtain the PPR for the NMLE using logistic regression analysis. In a new cohort of 106 medical students in 2018, we applied the formula for PPR to them to confirm the capability of the PPR and predicted students who will have a strong likelihood of failing the NMLE. Results Medical students who passed the NMLE had the following characteristics: younger age at admission, graduates of high schools located in the surrounding area, high scores in the graduation examination and in the comprehensive computer-based test provided by the Common Achievement Test Organization in Japan. However, total score of examination in pre-clinical medical sciences and Pre-CC OSCE score in the 4th year were not correlated with the PPR. Ninety-one out of 531 students had a strong likelihood of failing the NMLE between 2012 and 2017 and 33 of these 91 students failed NMLE. Using the PPR, we predicted 12 out of 106 students will have a strong likelihood of failing the NMLE. Actually, five of these 12 students failed NMLE. Conclusions The PPR can be used to predict medical students who have a higher probability of failing the NMLE. This prediction would enable focused support and guidance by faculty members. Prospective and longitudinal studies for larger and different cohorts would be necessary.

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Risk factors associated with academic difficulty in an Australian regionally located medical school

Cited by 17 publications

References 43 publications

Dealing with Academic Difficulty in Medical School: A Pilot Study

Dealing with Academic Difficulty in Medical School: A Pilot Study

Associations between demographic factors and the academic trajectories of medical students in Japan

Identifying and supporting students at risk of failing the National Medical Licensure Examination in Japan using a predictive pass rate

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