Drama to promote non‐verbal communication skills
Bibliographic record
Abstract
BACKGROUND: Non-verbal communication skills (NVCS) help physicians to deliver relationship-centred care, and the effective use of NVCS is associated with improved patient satisfaction, better use of health services and high-quality clinical care. In contrast to verbal communication skills, NVCS training is under developed in communication curricula for the health care professions. One of the challenges teaching NVCS is their tacit nature. In this study, we evaluated drama exercises to raise awareness of NVCS by making familiar activities 'strange'. METHODS: Workshops based on drama exercises were designed to heighten an awareness of sight, hearing, touch and proxemics in non-verbal communication. These were conducted at eight medical education conferences, held between 2014 and 2016, and were open to all conference participants. Workshops were evaluated by recording narrative data generated during the workshops and an open-ended questionnaire following the workshop. Data were analysed qualitatively, using thematic analysis. Non-verbal communication skills help doctors to deliver relationship-centred care RESULTS: One hundred and twelve participants attended workshops, 73 (65%) of whom completed an evaluation form: 56 physicians, nine medical students and eight non-physician faculty staff. Two themes were described: an increased awareness of NVCS and the importance of NVCS in relationship building. Drama exercises enabled participants to experience NVCS, such as sight, sound, proxemics and touch, in novel ways. Participants reflected on how NCVS contribute to developing trust and building relationships in clinical practice. DISCUSSION: Drama-based exercises elucidate the tacit nature of NVCS and require further evaluation in formal educational settings.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".