Flexible Approaches to Using Online Case Data When Coupled with Textbook Based Case Studies in Medical Sciences Teaching and Learning
Bibliographic record
Abstract
Case studies are commonly used to anchor important theoretical concepts with carefully guided use of practical experience. This session explored approaches and methodologies for effective and creative use of coupling online case study resources with text book case studies in order to enhance learning. An illustrative example referred to use of a textbook of case studies in One Health (i.e., the factors and health outcomes related to the interaction of animals, humans, and their environment) to be published in 2016 that is coupled with online data, visual resources, and testimonials. Coupling textbooks of medical case studies in particular with online additional data is not new, although there was no record found of a learning situation using medical case studies in which prescriptive step-by-step instructional guidance for using online data is deliberately avoided and instead students are encouraged to rely on their own creativity. Benefits of avoiding a prescriptive approach include a more realistic learning experience for clinicians, latitude for use of personally preferred learning styles, and more opportunities for creativity in the teaching and learning process. Participants in the session reflected on their own teaching and learning style preferences to contribute to discussion of how required use of online case study data might stimulate or suppress their creativity, both for teachers and students. Barriers included lack of consistency in audience demographic making it more challenging to provide a consistent learning experience and poor attention to context of application of case study lessons; catalysts included some degree of guided process for stimulating use of case study materials, supervision of discussion to facilitate sufficient interaction for learning, and adequate funding for preparing and hosting case study materials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.148 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.009 | 0.029 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".