Preparing Expert Communicators: Bridging the Gap Between the Basic Science of Communication and Health Professions Education
Bibliographic record
Abstract
Communicating with patients is an essential skill for health care providers and impacts the well-being of individuals and the health care system broadly. However, communication skills can be challenging to teach and perform due to the complexity and ambiguity of patient-provider interactions. Research has demonstrated the value of instructional approaches that foster adaptive expertise – a form of practice that enables providers to use their knowledge flexibly and innovatively when facing novel and complex problems. The opportunity to engage these approaches to support communication is a growing area of interest. This dissertation draws on the theories of adaptive expertise and cognitive integration to guide an exploration of communication training for health professionals. In this dissertation I seek to identify the foundational knowledge, the ‘basic science’, underpinning patient-provider interactions and explore the ways that current communication education supports learners in building this conceptual knowledge and developing the ability to communicate as adaptive experts in clinical practice. This examination considered both the communication training content covered and the pedagogical approaches employed in clinical and classroom contexts. My first study, a critical scoping review of the literature, identified six conceptual groupings that meaningfully describe the conceptual knowledge necessary for effective patient-provider communication. My second study used these groupings as a guiding framework to explore communication training in a clinical context, using a case study design. Here I identified the management of transactional and relational goals as the core competency exemplifying communication expertise and observed how expert communicators encapsulate conceptual communication knowledge to support their clinical practice. My final study explored conceptual knowledge for knowledge that is embedded within structured educational materials, building upon the findings of the first two studies using a framework analysis approach. This work identified content that was unique to formal education and content that was absent from these materials. Together these three studies provide a foundation for the basic science of communication to support an integrated understanding of patient-provider communication, a rich description of what adaptive expertise looks like in the context of patient-provider communication, and the strengths and limitations of different teaching/learning contexts for developing as adaptive experts in the context of patient-provider communication.
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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 | 0.030 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".