Effectiveness of Bi-lingual Multidisciplinary Simulation-based Training in Improving Communication and Breaking Bad-News Skills
Bibliographic record
Abstract
Background: Healthcare worker (HCW)-patient communication is an essential element of every patient’s journey, and evidence links good communication with favourable patient experiences and outcomes. Simulation-based training (SBT) is a promising and effective tool to improve such communication. Aim: To develop a bilingual SBT programme in communication skills for all HCWs in an academic tertiary hospital, to improve patient care, experiences and outcomes. Methods: This was a quasi-experimental design, conducted in 2018 at King Abdulaziz University (KAU). We designed and delivered a bilingual, simulation-based, full-day course for HCWs (both clinical and administrative), and measured its impact by comparing pre- and post-course test scores, participant feedback, and instructor performance satisfaction indices. Results: We trained 318 HCWs over 15 days, using 10 instructors. Post-test scores showed individual and overall improvement. The average scores were 26.6% (14-40%) for the pre-test and 55.8% (37-70%) for the post-test, with an average improvement of 29% (P<0.005). Participant feedback was 77% positive and in favour of more training. The average instructor performance satisfaction score was 96.2% (92-99%). Conclusion: We demonstrated the positive impact of SBT on communication skills for both clinical and administrative HCWs. We also demonstrated the sustainability and scalability of this course.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".