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Educating Future Physicians for a Minority Population

2002· article· en· W2060482396 on OpenAlexaffabout
Jeanne Drouin, P Jean

Bibliographic record

VenueAcademic Medicine · 2002
Typearticle
Languageen
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFrenchMedical educationPopulationHealth careMedicinePsychologyFamily medicineNursingPolitical scienceHumanities

Abstract

fetched live from OpenAlex

The Faculty of Medicine at the University of Ottawa has recently developed a French-language undergraduate medical education stream in order to train physicians for the francophone minority population of the province of Ontario. This new program was planned with the following societal requirements in mind: the need to receive health care in one's mother tongue, the need to have physicians who know the community, and the expectation of receiving good medical care in an ambulatory setting. A systematic educational planning model was used in order to develop three educational innovations in response to these needs and expectations: a communication skills laboratory; early student exposure to the ambulatory, primary care setting for development of clinical skills; and clerkship rotations in a francophone community hospital. Program developers provided ongoing faculty development activities in order to prepare francophone faculty for their new roles. They also considered student participation in program development an essential element of its success. The program has positive outcomes both within and outside the Faculty of Medicine. These include an enrichment effect on the English-language stream, an increased interest in medical education, student satisfaction with their community hospital clerkship rotations, and the recognition of the educational program as a national resource for francophone minority groups. Medical schools that serve minority population groups may benefit from the experience gained at the University of Ottawa.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.627

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.130
GPT teacher head0.493
Teacher spread0.363 · 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 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

Citations15
Published2002
Admission routes2
Has abstractyes

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