Evaluating final-year medical students' communication skills using an observational rating scale in Shiraz medical school
Bibliographic record
Abstract
Background: The way doctors communicate with their patients has basic effects on patient outcomes. The aim of this study was to use a rating scale based on Calgary-Cambridge guide for evaluating doctor and patient communication skills. Methods: This was a cross-sectional performance-based assessment study, done during 2016-2017 in Shiraz Medical School. This project was performed on 125 last year medical students (Interns). An observational rating scale was used based on Calgary-Cambridge guide to medical interview. The validity and reliability of the rating scale was determined in our previous study. The researcher observed the interns’ behavior and scored the scale based on the performance of each intern. The scores of each item in the rating scale were from 1 (very weak) to 5 (excellent). Results: Of the 128 interns who were included in the study, 81 (63%) were women and 47 (37%) were men. The general communication skill score between doctors and patients in this study was 3.18 out of 5. The level of communication skills of the female interns was higher than the male interns. This difference was statistically significant (P<0001). The highest average communication skill between the doctor and the patient was in the ward with low crowding. This difference was statistically significant (P<0001). Discussion: The mean communication skills score in this study showed that communication skills training was insufficient and should be emphasized more. Medical students’ preparation in communication skills must put emphasis on identifying opportunities to advance communication skills that improve their future patient experience. It is necessary to have emphasis on communication skills training in the core curriculum.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".