Reimagining resources
Bibliographic record
Abstract
Case-based learning (CBL) scenarios in medical education have been a long-standing teaching practice, helping to marry the theoretical and practical aspects of medicine in students’ minds. However, partly due to rapidly progressing technology and globalisation, there is a growing generational disconnect between medical educators and students that needs addressing. Existing literature has highlighted that the involvement of students as partners in the development of educational resources can aid in bridging the divide and engaging students. This case study was a partnership between students and faculty within the Doctor of Medicine program at the University of Queensland. It aimed to co-create a CBL scenario and integrate it into the curriculum. The findings reveal that the co-created scenario was more positively received by students compared to faculty-developed scenarios. This approach demonstrates the potential of co-creation as a pedagogical strategy to foster engagement and address evolving educational needs in the medical curriculum.
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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.010 |
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".