Enhancing Engagement With Endocrine Guidelines and Fostering Medical Student Interest Through Concise Medical Information Cines: Qualitative Co-Design Study
Bibliographic record
Abstract
Background: There is a need to modernize the dissemination of clinical guidelines, making them more accessible and engaging for health care professionals. Concise Medical Information Cines (CoMICs) are peer-reviewed videos created by medical students that distill complex guidelines into learner-friendly visuals. Objective: This study aimed to describe the process of co-designing an audiovisual version of a clinical guideline and explore the experiences of co-designing audiovisual guideline summaries using the CoMICs model. Methods: A 4-part CoMICs series on glucocorticoid-induced adrenal insufficiency was codeveloped by clinicians and medical students through 10 iterative steps. A patient version of these CoMICs was then created in multiple languages. Semistructured interviews with authors, reviewers, and student collaborators assessed the clarity, usability, trustworthiness, and educational value of these CoMICs. Reflexive thematic analysis then identified key themes. Results: CoMICs improved guideline accessibility, comprehension, and global adaptability, while the collaborative process promoted interdisciplinary learning and underscored the efficacy of audiovisual tools for complex content. Student collaborators reported greater confidence in interpreting and communicating clinical guidance, renewed interest in endocrinology, and a deeper appreciation of its academic dimensions. Conclusions: Cocreating audiovisual resources, such as CoMICs, enhances guideline dissemination. Student involvement can foster curiosity, encourage academic career pathways, and reshape engagement with evidence-based medicine.
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 | 0.049 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".