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Record W4205514753 · doi:10.3389/feduc.2021.788909

Pedagogical and Personal Experiences Motivating Indigenous Students to Pursue Medical Studies

2022· article· en· W4205514753 on OpenAlexafffund
Tanya Chichekian, Léa Bragoli‐Barzan, Sonia Rahimi

Bibliographic record

VenueFrontiers in Education · 2022
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversité du Québec à MontréalUniversité de Sherbrooke
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousThematic analysisHealth careIdentification (biology)Qualitative researchMedical educationSelf-determination theoryPsychologyPublic relationsPedagogySociologyMedicinePolitical scienceSocial scienceAutonomy

Abstract

fetched live from OpenAlex

When it comes to accessibility to healthcare and medical education, inequalities prevail within ethnically diverse populations, especially among Indigenous Peoples. The main objective of this qualitative study was to explore how Indigenous female medical students’ motivations played a role in their pursuit of a medical career. We use the Self-Determination theory to frame this study and conduct individual open-ended interviews with four female Indigenous students’ regarding their motivational sources for applying to medical school. We provide an illustrative scenario for each identified motivational source through a thematic analysis. Results revealed two main sources of motivations: (Jones et al., Acad Med, 2019, 94 (4), 512–519) pedagogical experiences (i.e., contextual factors at school, academic interests, and opportunities) and (Sloof et al., Med Educ, 2021, 55 (5), 653) personal experiences (i.e., family support and influence, and future career prospects). Indigenous students’ personal experiences were more prevalent and described autonomous forms of motivations, whereas sources of motivation that were pedagogically oriented reflected more controlled forms of motivations. Different types of motivations can be useful, but not sufficient for the tipping point when the time comes for medical school applications. Learning about specialized Indigenous streams for admissions played the most influential role in students’ decision-making to pursue medical studies. Promoting the visibility of the Indigenous stream coupled with the identification of different forms of motivation could be informative when outlining evidence-based recommendations with the aim of improving inequalities within the health professions.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.426
Teacher spread0.385 · 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 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

Citations2
Published2022
Admission routes2
Has abstractyes

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