“Talk to them”: Teaching communication skills to students of Traditional Chinese Medicine
Bibliographic record
Abstract
Objective : To explore students’ perceptions of the efficacy and value of teaching communication skills in a health professional course for Traditional Chinese Medicine (TCM). Methods : Individual surveys were used to evaluate students’ self-assessment of their communication skills pre and post a communication subject in a four-year degree course in a Bachelor of Health Science in TCM at a large metropolitan university in Australia. Quantitative and qualitative data were collected. Results : Findings indicate that students recognise the need for good communication skills training as part of their professional training and self-reported that their communication skills improved following a semester of study of a communication subject. Conclusions : One of the primary components driving increasing demand for complementary and alternative medicine (CAM), which includes TCM, is that consumers place a high value on effective communication and quality engagement with their CAM provider. Communication skills are often seen as the cornerstone of good health care practice, patient recovery and practitioner job satisfaction. Implementing a focused communication skills component in health professional educational programs, including those for TCM, is therefore essential. Further research is needed to explore the retention of these skills throughout health professionals’ degree programs and after graduation and clinical experience, to evaluate the effectiveness and sustainability of personal communication skills education. Practice implications : Communication skills training should be incorporated into health care profession curricula, early in the program and integrated with clinical exposure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".