Does a Rater's Professional Background Influence Communication Skills Assessment?
Bibliographic record
Abstract
There is increasing pressure in veterinary education to teach and assess communication skills, with the Objective Structured Clinical Examination (OSCE) being the most common assessment method. Previous research reveals that raters are a large source of variance in OSCEs. This study focused on examining the effect of raters' professional background as a source of variance when assessing students' communication skills. Twenty-three raters were categorized according to their professional background: clinical sciences (n=11), basic sciences (n=4), clinical communication (n=5), or hospital administrator/clinical skills technicians (n=3). Raters from each professional background were assigned to the same station and assessed the same students during two four-station OSCEs. Students were in year 2 of their pre-clinical program. Repeated-measures ANOVA results showed that OSCE scores awarded by the rater groups differed significantly: (F(matched_station_1) [2,91]=6.97, p=.002), (F(matched_station_2) [3,90]=13.95, p=.001), (F(matched_station_3) [3,90]=8.76, p=.001), and ((Fmatched_station_4) [2,91]=30.60, p=.001). A significant time effect between the two OSCEs was calculated for matched stations 1, 2, and 4, indicating improved student performances. Raters with a clinical communication skills background assigned scores that were significantly lower compared to the other rater groups. Analysis of written feedback provided by the clinical sciences raters showed that they were influenced by the students' clinical knowledge of the case and that they did not rely solely on the communication checklist items. This study shows that it is important to consider rater background both in recruitment and training programs for communication skills' assessment.
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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 | 0.037 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".