An Overview of the Classification of Doctors’ Questioning in Doctor-Patient Conversations
Bibliographic record
Abstract
Questioning is the fundamental part in doctor-patient conversations. For accurate diagnosis and treatment, doctors usually seek information by questioning. The research of questioning is, therefore, essential to research into doctor-patient communication. It not only enhances the understanding of doctors’ information seeking, but improves patients’ ability of information provision. As to the research on questioning, knowing well of classification of questioning is the first step to comprehensively understand doctor-patient communication per se. Scholars generally study the classification of doctors’ questioning from four perspectives. a) In terms of conversational process, there are mainly social history taking question, medical question, and psychological question; b) In terms of linguistic markers, there are wh- question, inverted auxiliary question and tag question; c) In terms of contents, there are open question and closed question; d) In terms of functions, information function and speech function are considered. Forms of each type of doctors’ questioning vary with different perspectives, but there are no “good” questioning and “bad” questioning. All kinds of questioning are not isolated but related, even overlapping. Doctors’ choice for different kinds of questioning depends on their diverse requirements.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".