Using an objective structured video exam to identify differential understanding of aspects of communication skills
Bibliographic record
Abstract
BACKGROUND: Effective communication in health care is associated with patient satisfaction and improved clinical outcomes. Professional schools increasingly incorporate communication training into their curricula. The objective structured video exam (OSVE) is a video-based examination that provides an economical way of assessing students' knowledge of communication skills. This study presents a scoring strategy that enables blueprinting of an OSVE to consensus guidelines, to determine which aspects of communication skills create the most difficulty for students to understand and to what degree understanding improves through experiential communication skills training. METHODS: Five interactions between a healthcare professional and client were scripted and filmed using standardized patients. The dialogues were mapped onto the Kalamazoo consensus statement by having five communication experts view each video and identify effective and ineffective use of communication skills. Undergraduate students enrolled in a communications course completed an OSVE on three occasions. RESULTS: A total of 79 students completed at least one testing session. The scores assigned supported the validity of the scoring strategy as an indication of knowledge growth. Considerable variability was observed across Kalamazoo sub-domains. CONCLUSION: With further refining, this scoring approach may prove useful for educators to tailor their education and assessment practices to specific consensus guidelines.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".