Online learning in paediatrics: a student‐led web‐based learning modality
Bibliographic record
Abstract
BACKGROUND: undergraduate medical education is shifting away from traditional didactic methods towards a more self-directed learning environment. E-learning has emerged as a vital learning modality that allows students to apply key principles to practical scenarios in a truly personalised approach. CONTEXT: at the University of Alberta, paediatrics is taught longitudinally, with lectures distributed throughout the preclinical curriculum and concentrated in the 8-week paediatric clinical clerkship. As a result, students entering clerkship lack core foundational knowledge and clinical skills. INNOVATION: PedsCases (http://www.pedscases.com) is a student-driven interactive website designed to achieve the learning outcomes identified by the competency-based paediatric curriculum. This open-access e-learning tool is a comprehensive peer-reviewed learning resource that incorporates various learning modalities. Material is student generated and peer reviewed by staff paediatricians to ensure validity, accuracy and usefulness. After 17 months, PedsCases contains 216 questions, 19 cases, 11 flashcard-type quizzes, 11 podcasts and two clinical videos, and has had 2148 unique visitors from 73 different countries. PedsCases is one of the top five references returned by Internet search engines for the phrase 'paediatrics for medical students'. IMPLICATIONS: PedsCases is a collaborative resource created for and by medical students that provides an opportunity for active self-directed learning while disseminating knowledge in an evidence-based, interactive and clinically relevant fashion. PedsCases encourages students to take an active role in their education and drive medical education initiatives in response to the evolving curriculum. As the focus of medical education shifts towards independent learning, student-led educational tools such as PedsCases have emerged as essential resources for students.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".