Augmenting Flexnerism Via Twitterism: Need for Integrating Social Media Application in Blueprinting Pedagogical Strategies for Undergraduate Medical Education
Bibliographic record
Abstract
BACKGROUND: Flexnerism, or "competency-based medical education," advocates that formal analytic reasoning, the kind of rational thinking fundamental to the basic sciences, especially the natural sciences, should be the foundation of physicians' intellectual training. The complexity of 21st century health care requires rethinking of current (medical) educational paradigms. In this "Millennial Era," promulgation of the tenets of Flexnerism in undergraduate medical education requires a design and blueprint of innovative pedagogical strategies, as the targeted learners are millennials (designated as generation-Y medical students). OBJECTIVE: The aim of this proof-of-concept study was to identify the specific social media app platforms that are selectively preferred by generation-Y medical students in undergraduate medical education. In addition, we aimed to explore if these preferred social media apps can be used to design an effective pedagogical strategy in order to disseminate course learning objectives in the preclinical phase of a spiral curriculum. METHODS: A cross-sectional survey was conducted by distributing a 17-item questionnaire among the first- and second-year medical students in the preclinical phase at the Mohammed Bin Rashid University of Medicine and Health Science. RESULTS: The study identified YouTube and WhatsApp as the social media app platforms preferred by generation-Y medical students in undergraduate medical education. This study also identified the differences between female and male generation-Y medical students in terms of the use of social media apps in medical education, which we believe will assist instructors in designing pedagogical strategies to integrate social media apps. In addition, we determined the perceptions of generation-Y medical students on the implementation of social media apps in medical education. The pedagogical strategy designed using social media apps and implemented in the Biochemistry course was well accepted by generation-Y medical students and can be translated to any course in the preclinical phase of the medical curriculum. Moreover, the identified limitations of this study provide an understanding of the gaps in research in the integration of social media apps in a medical curriculum catering to generation-Y medical students. CONCLUSIONS: 21st century medical education requires effective use of social media app platforms to augment competency-based medical education: Augmentation of Flexnerism in the current scenario is possible only by the adaptation of Twitterism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".