Early Detection and Intervention: A Framework for Preventing Academic Failure in Medical Students
Abstract
Academic failure is multifactorial with personal, institutional, and societal factors. Identification after high stake assessments comes too late for meaningful interventions. There is limited data to predict academic failure at an early stage. This qualitative exploratory study aimed to identify such students using a predictive framework. Using purposive sampling, twenty-seven participants (8 academic failures, 8 high achievers, and 7 medical teachers) were enrolled after informed consent and ethical approval. One-to-one interviews with eight academic failures and two focused group discussions (FGDs), one with the high achievers and the teachers were conducted online using a validated questionnaire. Thematic analysis and blended coding was done manually with member checking and triangulation. Key predictors included poor self-regulation, procrastination, emotional imbalance, low self-efficacy, cognitive overload, and non-reflective practices. Key components of the framework suggested are documentation of students’ prior academic results and professional choice, at the time of admission as well as behavioral traits and performance in formative assessments. Close observation of procrastination, emotional state, and self-efficacy during small group discussions by teachers followed by feedback will identify students at risk. Reflective practices by both students and teachers during feedback sessions will uncover hidden learning gaps. In conclusion, “potential academic failures” can be identified and supported by observation, documentation, and reflective practices by teachers and students using proposed framework.
doi: https://doi.org/10.12669/pjms.41.3.10883
How to cite this: Rashid A, Yasmeen R, Khan RA. Early Detection and Intervention: A Framework for Preventing Academic Failure in Medical Students. Pak J Med Sci. 2025;41(3):919-922-. doi: https://doi.org/10.12669/pjms.41.3.10883
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
