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Record W2594551604 · doi:10.18584/iipj.2017.8.1.5

Indigenous Student Matriculation into Medical School: Policy and Progress

2017· article· en· W2594551604 on OpenAlexaffvenueabout
Kathy Sadler, Marjorie Johnson, Candace Brunette, Lorne J. Gula, Mary Ann Kennard, David Charland, Gary Tithecott, Gerry Cooper, Michael Rieder, Chris Watling, Carol P. Herbert, Bertha García, Robert Hammond

Bibliographic record

VenueInternational Indigenous Policy Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsWestern University
Fundersnot available
KeywordsIndigenousMatriculationFormative assessmentCurriculumMedical educationMedical schoolHealth careProcess (computing)MedicinePolitical scienceSociologyPedagogy

Abstract

fetched live from OpenAlex

Access to health care remains suboptimal for Indigenous people in Canada. One contributing factor is the longstanding undersupply of Indigenous physicians. Despite awareness of this issue, underrepresentation in medical schools continues. In 2002, Schulich School of Medicine and Dentistry (SSMD) policies were modified to enhance access for Indigenous students. This article describes our school’s continuing journey of policy and process revision, formative collaborations, early learner outcomes, and lessons learned towards this goal. In the first 10 years, SSMD matriculated 15 additional Indigenous students via this new stream. All candidates were successful in the undergraduate medical curriculum, licensing examinations, and residency match. The majority were attracted to primary care specialties, training programs affiliated with SSMD, and practices in southern Ontario. While the process and curriculum have revealed their potential, its capacity is not being maximized.

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)0.020
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score0.798

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0090.003
Open science0.0040.007
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0110.001

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 head0.037
GPT teacher head0.521
Teacher spread0.485 · 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 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

Citations12
Published2017
Admission routes3
Has abstractyes

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