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Record W4312531377 · doi:10.1016/j.ijedro.2022.100212

Indigenous students’ experiences regarding the utility of university resources during medical training

2022· article· en· W4312531377 on OpenAlexafffundabout
Tanya Chichekian, Catherine Maheux

Bibliographic record

VenueInternational Journal of Educational Research Open · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversité de Sherbrooke
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousMedical educationRepresentation (politics)Traditional knowledgePsychologySociologyMedicinePolitical scienceEcologyLaw

Abstract

fetched live from OpenAlex

The Association of Faculties of Medicine of Canada recognizes that the representation of Indigenous people in medicine has historically been low. As a response to address the latter, several support structures have been put in place in universities for Indigenous students in medical programs. In this phenomenological study, we conducted open-ended, semi-structured interviews with four Indigenous female students in medicine regarding their experiences interacting with university resources. Peer mentorships, opportunities for clinical experiences in proximity to Indigenous communities, and an appointed Indigenous contact person were the resources that contributed most to supporting participants during medical training. Although a generalized positive attitude was held toward all available university resources, Indigenous students also expressed concern regarding the utility of certain resources, mainly due to their accessibility and restrictions imposed at the institutional level. Implications are discussed regarding Indigenous students’ sense of belonging in non-Indigenous universities.

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)0.006
metaresearch head score (Gemma)0.011
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 score0.014
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.009
Scholarly communication0.0050.002
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.113
GPT teacher head0.475
Teacher spread0.362 · 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

Citations1
Published2022
Admission routes3
Has abstractyes

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Same venueInternational Journal of Educational Research OpenSame topicIndigenous Health, Education, and RightsFrench-language works237,207