Coming of age as communicators: differences in the implementation of common communications skills training in four residency programmes
Bibliographic record
Abstract
OBJECTIVES: To determine thematic similarities and differences in the implementation of common-content communications skills training (CST) in medicine, surgery, paediatrics, and obstetrics and gynaecology residency programmes. METHODS: Communications skills training based upon the Kalamazoo consensus statement of communication skills in the clinical encounter was implemented in 4 residency programmes. Field notes of the CST sessions in each programme were analysed and coded for themes, considering the domains of Context, Input, Process and Product ('CIPP' methodology). Immediate learning outcomes were quantitatively assessed using retrospective pre/post methodology. RESULTS: Important differences were noted in the implementation of CST in the 4 disciplines. The 2 surgical disciplines showed relatively less reflective language and greater concentration on straight skill acquisition, whereas the 2 medical disciplines concentrated on the residents' role as teachers of communication skills for buy-in. Thematic similarities between disciplines included similar challenges to being good communicators in practice, as identified by residents (e.g. inadequate time and space), as well as lack of formal training. Quantitative learning outcome data from the educational intervention were significant in all groups (P < 0.05). CONCLUSIONS: Common material in CST can be adapted to different disciplines. By analysing for thematic similarities and differences in implementation in the 4 disciplines, a picture of different pedagogic 'subcultures' emerged, with different behavioural norms and values related to the doctor's role as communicator. In shared core competency training, it may be useful to consider these differences in planning, so that the training may be both sensitive to the behavioural norms of different disciplines, and effective.
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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 | 0.004 | 0.018 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".