A Linguistic Analysis of the Politeness Strategies Used in Doctor-Patient Discourse
Bibliographic record
Abstract
Effective and well-organized communication between the doctors and patients plays a fundamental clinical role and results patient’s early healing of body and mind. Most of the patients’ annoyance and complaints are observed due to the communication gap found in the doctors-patients discourse. This research study was carried out to locate whether divergences occur or not in the utilization and integration of politeness strategies used in medical discourse. The aim of the study is to discover and highlight the communication gap between doctors and patients. In this ethno-linguistic exploratory study, both quantitative and qualitative data were obtained to find answers of the formulated research questions. Findings reveal that majority of the doctors unduly focus on exhibiting power and dominance over patients in their talks made with them. It was also found that doctors mostly use the strategy of ‘Bald on Record’ with both male and female patients; and wide majority of patients showed dissatisfaction with doctors’ conversation during diagnosing, treatment and follow-up visits. It is recommended that doctors should be made liable to execute good politeness and ethical strategies while communicating with patients for medical purposes.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".