Implementing a communication skills programme in medical school: needs assessment and programme change
Bibliographic record
Abstract
INTRODUCTION: Communication skills training (CST) in medicine, once considered a minor subject, is now ranked a core clinical skill. To assess the state of formal and informal CST at Dalhousie Medical School a needs assessment was undertaken in 1997 with the goal of using these findings to plan and implement a new communication skills curriculum. OBJECTIVES: This article briefly describes the relevant findings of the needs assessment, the subsequent development of an integrated cross curriculum CST programme, and early programme evaluation results. METHOD: Surveys were completed by undergraduates at the end of pre-clinical (n=65), and clinical phases (n=82), residents (n=54), and faculty (n=117). Results revealed learners' and faculty's appreciation of the importance of CST, learners' assessment of training weaknesses in the delivery of CST, learners' weakness in higher order patient--doctor communication skills, and faculty weakness in assessing learners' communication skills competency. The results also indicated that CST was generally not being addressed either formally or informally in clinical medical education. RESULTS: The paper describes and discusses the subsequent implementation (beginning in 1998) of CST into the medical school curriculum. There is a description of programme development and evaluation at the pre-clinical, clerkship and postgraduate levels, a description and discussion of faculty development, and discussion of the importance of financial and administrative support for the programme. CONCLUSION: Programme evaluation results at all levels are positive.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".