Teaching Professionalism: Comparing Written and Video Case-Studies
Bibliographic record
Abstract
Purpose: Professionalism is a difficult concept to teach to healthcare professionals. Case-studies in written and video format have demonstrated to be effective teaching tools to improve a student’s knowledge, but little is known about their impact on student behaviour. The purpose of this research study was to investigate and compare the impact of the 2 teaching tools on a student’s behaviour during a simulation. Method: A 3-stage mixed method study was conducted with senior Medical Laboratory Science (MLS) undergraduate students. All students were randomly divided into a Written Group or Video Group to attend a mandatory professionalism workshop focused on bullying and gossip. Twenty-six students completed the voluntary assignment and 21 students participated in the voluntary group simulations. Thematic analysis was performed on the assignments and simulation. Frequencies of themes were calculated. A Group Simulation Assessment Rubric was used to score simulations and calculate an adjusted group performance average (AGPA). Results: The assignment demonstrates that students from both groups obtained a theoretical understanding of how to resolve gossip and bullying. From the Written Group and Video Group, 70%/18% of students discouraged/resolved gossiping and 80%/63% prevented bullying. The mean AGPA for the Written Group and Video Group was 5.4 and 4.9 respectively ( t(5) = 1.5, P = .2). Discussion: Students can successfully apply knowledge they have gained in written and video case-studies focused on the professionalism topics of bullying and gossip to a hypothetical situation. However, a discrepancy in their actions was found during the simulations. The data from the study suggests that written and video case-studies do not have different impacts on a student’s behaviour.
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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 | 0.025 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".