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Record W2913499104 · doi:10.5539/ells.v9n1p80

A Linguistic Analysis of the Politeness Strategies Used in Doctor-Patient Discourse

2019· article· en· W2913499104 on OpenAlexvenueno aff
Muhammad Arfan Lodhi, Farah Naz, Sumaira Yousaf, Syeda Nimra Ibrar

Bibliographic record

VenueEnglish Language and Literature Studies · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitenessConversationExploratory researchDominance (genetics)PsychologyQualitative researchMedicineMedical educationSocial psychologySociologyNursingLinguisticsSocial scienceCommunication

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.193
Threshold uncertainty score0.467

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.298
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

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