Do communication training programs improve students’ communication skills? - a follow-up study
Bibliographic record
Abstract
BACKGROUND: Although it is taken for granted that history-taking and communication skills are learnable, this learning process should be confirmed by rigorous studies, such as randomized pre- and post-comparisons. The purpose of this paper is to analyse whether a communication course measurably improves the communicative competence of third-year medical students at a German medical school and whether technical or emotional aspects of communication changed differently. METHOD: A sample of 32 randomly selected students performed an interview with a simulated patient before the communication course (pre-intervention) and a second interview after the course (post-intervention), using the Calgary-Cambridge Observation Guide (CCOG) to assess history taking ability. RESULTS: On average, the students improved in all of the 28 items of the CCOG. The 6 more technically-orientated communication items improved on average from 3.4 for the first interview to 2.6 in the second interview (p < 0.0001), the 6 emotional items from 2.7 to 2.3 (p = 0.023). The overall score for women improved from 3.2 to 2.5 (p = 0.0019); male students improved from 3.0 to 2.7 (n.s.). The mean interview time significantly increased from the first to the second interview, but the increase in the interview duration and the change of the overall score for the students' communication skills were not correlated (Pearson's r = 0.03; n.s.). CONCLUSIONS: Our communication course measurably improved communication skills, especially for female students. These improvements did not depend predominantly on an extension of the interview time. Obviously, "technical" aspects of communication can be taught better than "emotional" communication skills.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".