Assessors for communication skills: SPs or healthcare professionals?
Bibliographic record
Abstract
INTRODUCTION: The complexity of modern medicine creates more challenges for teaching and assessment of communication skills in undergraduate medical programme. This research was conducted to study the level of communication skills among undergraduate medical students and to determine the difference between simulated patients and clinical instructors' assessment of communication skills. METHODS: This comparative study was conducted for three months at the Clinical Skills and Simulation Centre of the International Medical University in Malaysia. The modified Calgary-Cambridge checklist was used to assess the communication skills of 50 first year and 50 second year medical students (five-minutes pre-recorded interview videos on the scenario of sore throat). These videos were reviewed and scored by simulated patients (SPs), communication skills instructors (CSIs) and non-communication skills instructors (non-CSIs). RESULTS: Better performance was observed among the undergraduate medical students, who had formal training in communication skills with a significant difference in overall scores detected among the first and second year medical students (p = 0.0008). A non-significant difference existed between the scores of SPs and CSIs for Year 1 (p = 0.151). CONCLUSIONS: The SPs could be trained and involved in assessment of communication skills. Formal training in communication skills is necessary in the undergraduate medical programme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".