Partial compensatory scoring model in integrated assessment
Bibliographic record
Abstract
information, networking, collaboration, examination review, etc.).For the next hour, students were divided into small groups.Three cases illustrating issues related to professionalism, chosen from the Canadian Federation of Medical Students (CFMS) Guide to Medical Professionalism: Recommendations for Social Media (www.cfms.org),were presented and discussed.Each group then presented its perceptions of the case and inter-group discussion was encouraged.At the end, the case recommendation from the CFMS guide was read out loud and debated.What lessons were learned?All francophonestream Year 1 medical students attended this course (n = 48).A quick survey conducted during the session using socrative.com('Which social media tool do you use most often?')revealed that Facebook was the most frequently used tool (by 41 of 48 students).The curricular evaluation of the session by students was extremely positive, earning a mean of 4.9 of a maximum score of 5.According to the students' comments, the session was judged to be 'pertinent', 'of actuality' and 'captivating'.Students appreciated the use of professionalism cases to promote discussion and interaction in small groups.Others highlighted the fact that although they were familiar with these tools, they were unaware of all aspects of their use in medical education.The fact that students were allowed to express their opinions during the discussion of the cases without being forced to choose a solution was cited as an important strength of the session.The interactive, case-based session taught professionalism hand-in-hand with the potential benefits of social media tools and, more importantly, allowed future doctors to make their own responsible decisions regarding different online professionalism-related dilemmas.This positive experience illustrates the value and importance of running a short session introducing aspects of the use of social media at the beginning of medical school.
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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 | 0.054 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".