The Basic Science of Patient–Physician Communication: A Critical Scoping Review
Bibliographic record
Abstract
PURPOSE: Strong verbal communication skills are essential for physicians. Despite a wealth of medical education research exploring communication skills training, learners struggle to become strong communicators. Integrating basic science into the curriculum provides students with conceptual knowledge that improves learning outcomes and facilitates the development of adaptive expertise, but the conceptual knowledge, or "basic science," of patient-provider communication is currently unknown. This review sought to address that gap and identify conceptual knowledge that would support improved communication skills training for medical trainees. METHOD: Combining the search methodology of Arksey and O'Malley with a critical analytical lens, the authors conducted a critical scoping review of literature in linguistics, cognitive psychology, and communications to determine: what is known about verbal communication at the level of word choice in physician-patient interactions? Studies were independently screened by 3 researchers during 2 rounds of review. Data extraction focused on theoretical contributions associated with language use and variation. Analysis linked patterns of language use to broader theoretical constructs across disciplines. RESULTS: The initial search returned 15,851 unique studies, and 271 studies were included in the review. The dominant conceptual groupings reflected in the results were: (1) clear and explicit language, (2) patient participation and activation, (3) negotiating epistemic knowledge, (4) affiliative language and emotional bonds, (5) role and identity, and (6) managing transactional and relational goals. CONCLUSIONS: This in-depth exploration supports and contextualizes theory-driven research of physician-patient communication. The findings may be used to support future communications research in this field and educational innovations based on a solid theoretical foundation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.060 | 0.231 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.036 | 0.026 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".