Bridging the gap between patient agency and doctor authority: how power interacts with structure in the consultation
Bibliographic record
Abstract
Background Clear communication in the medical consultation is key to promoting patient autonomy. Doctors may empower patients to express ideas, raise concerns and collaborate in decision-making through the use of language showing the consultation structure, called verbal signalling. However, there is little research showing how taught verbal signalling behaviours are used in practice or how they promote patient agency. This project identifies how verbal signalling behaviours may empower patients. Methods This mixed-methods study analysed secondary data featuring 154 simulated consultations forming part of a postgraduate examination for doctors aspiring to become physicians. Consultation structure was identified through novel application of the Calgary-Cambridge Guide to the Medical Interview onto verbatim transcripts. Speech Act Theory, Conversation Analysis and Politeness Theory were innovatively combined to identify, code and analyse verbal behaviours signalling consultation structure, such as ‘signposts’. Identification and classification occurred on three levels: whether behaviours shared power by informing, inviting or instructing the patient; what information behaviours shared about consultation structure or content, and how power was manifested through language used in the behaviours. Results Varying structure was seen across the consultations, which was broadly not shared with patients. As predicted, verbal signalling behaviours were used to inform, invite and instruct, leading to an original taxonomy based on how verbal signalling behaviours involved patients. Behaviours focused on micro-level processes, like introducing questions, rather than broader agenda setting. Some deflected away from the patient agenda. The wide range of roles found led to the creation of a second original taxonomy based on behaviour functions. Conclusion Doctors used an extensive repertoire of verbal signalling behaviours to shape, maintain and enforce consultation structure. Contrary to their teaching, some behaviours limited rather than promoted the patient agenda. This research reveals how verbal behaviours taught to facilitate patient agency may be repurposed to retain doctor authority instead.
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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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".