Educators and practitioners’ perspectives in the development of a learning by concordance tool for medical clerkship in the context of the COVID pandemic
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has forced medical schools to create educational material to palliate the anticipated and observed decrease in clinical experiences during clerkships. An online learning by concordance (LbC) tool was developed to overcome the limitation of students' experiences with clinical cases. However, knowledge about the instructional design of an LbC tool is scarce, especially the perspectives of collaborators involved in its design: 1- educators who wrote the vignettes' questions and 2- practitioners who constitute the reference panel by answering the LbC questions. The aim of this study was to describe the key elements that supported the pedagogical design of an LbC tool from the perspectives of educators and practitioners. METHODS: A descriptive qualitative research design has been used. Online questionnaires were used, and descriptive analysis was conducted. RESULTS: Six educators and 19 practitioners participated in the study. Important to the educators in designing the LbC tool were prevalent or high-stake situations, theoretical knowledge, professional situations experienced and perceived difficulties among students, and that the previous workshop promoted peer discussion and helped solidify the writing process. Important for practitioners was standards of practice and consensus among experts. However, they were uncertain of the educational value of their feedback, considering the ambiguity of the situations included in the LbC tool. CONCLUSIONS: The LbC tool is a relatively new training tool in medical education. Further research is needed to refine our understanding of the design of such a tool and ensure its content validity to meet the pedagogical objectives of the clerkship.
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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 | 0.079 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".