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Record W2629261984 · doi:10.1075/intp.19.2.04lea

From concern for patients to a quest for information

2017· article· en· W2629261984 on OpenAlexaffabout
Yvan Leanza, Élias Rizkallah, Thomas Michaud-Labonté, Camille Brisset

Bibliographic record

VenueInterpreting International Journal of Research and Practice in Interpreting · 2017
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversité du Québec à MontréalUniversité Laval
Fundersnot available
KeywordsInterpreterVignetteSocializationObject (grammar)PsychologyMedical educationDiscourse analysisFocus groupSocial psychologyMedicineLinguisticsSociologyComputer science

Abstract

fetched live from OpenAlex

This study of social representations about interpreted medical consultations examines the discourse of French language focus groups (FGs), conducted in Quebec, with 22 third year medical students (4 FGs), 29 family medicine residents (4 FGs) and 47 experienced family physicians (5 FGs). The audio-recorded FGs were transcribed. Each discussed two video vignettes of interpreted consultations. Statistical textual analysis showed that the students’ discourse patterns differed by FG. Residents prioritized access to the patient’s culture via the interpreter, though recognizing the need to respect the patient-physician relationship. Senior physicians organized their discourse differently for each vignette, associating it with a ‘standard’ response: for them, the two main issues were the quest for information, which we relate to the medical socialization process; and the interpreter’s stances, in terms of how s/he is perceived by physicians and the role(s) s/he is seen to play in the consultation. Physicians tend to represent the interpreter as a controllable ‘object’, not a full-fledged healthcare professional.

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)0.011
metaresearch head score (Gemma)0.216
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.216
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0020.001
Research integrity0.0000.001
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 head0.186
GPT teacher head0.616
Teacher spread0.430 · 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 designNot applicable
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

Citations5
Published2017
Admission routes2
Has abstractyes

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