MétaCan
Menu
Back to cohort
Record W2970636963 · doi:10.3968/11077

Interpersonal Meaning in Doctors’ Interrogatives From the Respective of Systemic Functional Grammar

2019· article· en· W2970636963 on OpenAlexvenueno aff
Xi Luo

Bibliographic record

VenueCanadian social science · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsInterrogative wordMeaning (existential)Systemic functional grammarInterpersonal communicationInterrogativeChinaPerspective (graphical)PsychologyGrammarMedicineSocial psychologyLinguisticsPsychotherapistPolitical scienceLawComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Objectives : To carry out a systemic functional research on the interpersonal meaning realized in Chinese doctor-patient conversations from the perspective of doctors’ choice of interrogative. Methods: Data were randomly collected from conversations between doctors and outpatients in one hospital in China, while being analyzed in terms of the interpersonal meaning from the SFL perspective. Statistical analysis was conducted by using SPSS 17.0. Results: (1) Interrogatives are also favored in doctor-patient conversations in China as compared with findings from other studies (Brody, 1992; Smith, et al., 1998; Meeuwesen, et al, 2007), but Chinese doctors dominate in the whole process of diagnosis and treatment, exerting great influence on the patient. (2) Yes-no interrogatives are favored more by doctors practicing Traditional Chinese Medicine than by those in other clinic departments. (3) Both yes-no interrogatives and alterative interrogatives can be quickly responded to, but doctors in China usually ignore this, unaware of the importance of building harmonious interpersonal relationships. Practice implications: This research may enhance the efficiency of treatment and decrease medical disputes caused by bad communications.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.268
Teacher spread0.227 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCanadian social scienceSame topicLanguage, Discourse, Communication StrategiesFrench-language works237,207