Exploring patient ideas, concerns, and expectations in surgeon-patient consultations
Bibliographic record
Abstract
OBJECTIVES: This study explores patient perspectives (ideas, concerns, and expectations) in surgeon-patient consultations. METHODS: We examined 54 video-recorded consultations using applied conversation analysis. Consultations took place from 2012 to 2017 in an Australian metropolitan hospital clinic centre and involved seven surgeons across six specialties. RESULTS: Patient perspectives emerged in less than one third of consultations. We describe the initiation of and response to potential perspectives sequences, demonstrating how patients and surgeons co-construct these sequences when they do occur. CONCLUSIONS: Findings suggest a need for greater attention to supporting patient agency through explicit pursuit of patient perspectives. The implications extend to the Calgary-Cambridge Guide, suggesting that it may benefit from a focus on active pursuit and appropriate responsiveness to patient perspectives. PRACTICE IMPLICATIONS: This study highlights the need for surgeons to actively engage with the patient perspective offered in consultations, emphasising the importance of respect for the patient's knowledge and expectations to improve patient satisfaction and healthcare outcomes.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".