A theory-based curriculum design for remediation of residents’ communication skills
Bibliographic record
Abstract
PROBLEM: Residents requiring remediation are often deficient in communication skills, namely clinical interviewing skills. Residents have to digest large amounts of knowledge, and then apply it in a clinical interview. The patient-centered approach, as demonstrated in the Calgary-Cambridge model and Martin's Map, can be difficult to teach. Before implementing a remediation curriculum, the theoretical educational underpinnings must be sound; curriculum evaluation is often expensive. Before establishing metrics for curriculum evaluation, a starting point is to perform a mental experiment to test theoretical adherence. METHODS: This article describes an experiential remedial curriculum for communication skills. Educational theories of Kolb, Knowles, Bandura, and Bloom are used to design the curriculum into theory-based design components. CONCLUSIONS: Kolb's experiential cycle models the natural sequence of experiencing, teaching, and learning interviewing skills. A curriculum structured around this cycle has multiple intercalations with the above educational theories. The design is strengthened by appropriately timed use of education strategies such as learning contracts, taped interviews, simulations, structured reflection, and teacher role modeling. Importantly, it also models the form of the clinical interview format desired. Through understanding and application of contemporary educational theories, a program to remediate interviewing skills can increase its potential for success.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".