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Record W7096089443

doi:10.1093/fampra/cmm004 Family Practice Advance Access published on 5 February 2007 GPs ’ strategies in intercultural clinical encounters

2014· article· en· W7096089443 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationAutonomyAdaptation (eye)Qualitative researchClinical PracticeGlobal Positioning System
DOInot available

Abstract

fetched live from OpenAlex

tercultural clinical encounters. Family Practice 2007; 24: 145–151. Background. In North America and Europe, patients and physicians are increasingly likely to come from non-Western cultural backgrounds. The expectations of these patients may not match those of physicians. Objective. To identify strategies used by GPs with patients from cultures other than their own. Methods. We conducted a qualitative inductive study based on 25 semi-structured interviews with family physicians practising in Montreal, Canada. We elicited physicians ’ strategies when dealing with patients from a cultural background different from their own. We began by asking physicians to describe an encounter they found difficult and one they found easy. Results. Physicians reported three types of strategies: (i) insistence on patient adaptation to lo-cal beliefs and behaviours; (ii) physician adaptation to what he or she assumed patients wanted; and (iii) negotiation of a mutually acceptable plan. Individual physicians did not adopt the same strategy in all situations. Their choice of strategy depended on the topic. When dealing with is-sues they felt deeply about, such as the autonomy of women, many physicians insisted on pa-tient adaptation. Physicians used a patient-centred model of care, but had no framework to elicit

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)0.004
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.685
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.007
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.067
GPT teacher head0.449
Teacher spread0.382 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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