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Record W2105778641 · doi:10.1080/01421590310001653982

Communication skills, cultural challenges and individual support: challenges of international medical graduates in a Canadian healthcare environment

2004· article· en· W2105778641 on OpenAlexafffundabout
Pippa Hall, Suzan Dojeiji, Anna Byszewski, Meridith B. Marks

Bibliographic record

VenueMedical Teacher · 2004
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsMedical educationHealth careNegotiationCommunication skillsWork (physics)Focus groupCommunication skills trainingCultural competencePsychologyMedicineNursingPedagogyPolitical scienceSociology

Abstract

fetched live from OpenAlex

Physicians require good communication skills to develop effective patient-physician relationships. Externally funded international medical graduates (IMGs) move directly from their home countries to complete residency training at the University of Ottawa, Canada. They must learn quickly how to work with patients, families and colleagues. A detailed needs assessment was designed to assess IMGs' communication skill needs through focus groups, interviews and surveys with IMGs, program directors, allied healthcare professionals and experts in communication skills. There was a high degree of consensus amongst all participants concerning specific educational needs for communication skills and training issues related to the healthcare system for externally funded IMGs. Specific recommendations include (1) English-language skills; (2) how to get things done in the hospital/healthcare system; (3) opportunities to practise specific skills, e.g. negotiating treatment, (4) adequate support system for IMGs; (5) faculty and staff education on the cultural challenges faced by IMGs.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.167
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.079
GPT teacher head0.410
Teacher spread0.331 · 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 source (direct Gemma or distilled Codex), 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

Citations149
Published2004
Admission routes3
Has abstractyes

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