Network of medical school educators for best practices in case‐study Problem Based Learning (719.7)
Bibliographic record
Abstract
Medical schools are reforming curriculum to integrate all competencies throughout all years. Interdisciplinary student‐centered case‐study PBL curriculum should result in students: 1. Gaining proficiency and understanding the process in clinical problem solving. 2. Recognizing knowledge gaps, learning to use reliable information, applying new knowledge. 3. Interacting effectively with peers, demonstrating professionalism appropriate to physician/patient interactions. Many schools introduced variations into case‐study content and PBL format and produced numerous assessment tools. Accordingly, the project objective was to form a collaborative network of medical school PBL case‐study teachers to share best strategies in facilitating PBL and develop/revise assessment tools to better address student engagement, learning outcomes, sense of value of PBL. Visits to Indiana University School of Medicine Centers of Medical Education in Terre Haute, Lafayette, Gary, and to University of Louisville and Northern Ontario School of Medicine were conducted. Materials and ideas were also shared electronically. Results include establishment of a collaborative network, sharing best practices and curricular materials, initiation of collaborative projects. An outcome of this work will be a model for other schools who wish to adopt/reform a PBL case study course or curriculum. Grant Funding Source : Supported by an APS Teaching Career Enhancement Award (TCEA).
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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.037 |
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".