MétaCan
Menu
Back to cohort
Record W4311024480 · doi:10.1111/medu.15000

Navigating the burden of proof and responsibility: A narrative inquiry into Indigenous medical learners' experiences

2022· article· en· W4311024480 on OpenAlexaffabout
Sarah Burm, Sebastian Deagle, Christopher Watling, Lloy Wylie, Danielle Alcock

Bibliographic record

VenueMedical Education · 2022
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsWestern UniversityLondon Health Sciences CentreUniversity of TorontoDalhousie University
Fundersnot available
KeywordsIndigenousNarrativeMedical educationNarrative inquiryPsychologyPedagogySociologyMedicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Many medical schools have well-established admission pathways and programming to support Indigenous medical workforce development. Ideally, these efforts should contribute to attracting highly qualified Indigenous applicants which, in turn, may improve accessible, quality care for Indigenous people. However, it is difficult to evolve and tailor these approaches without a situated understanding of Indigenous learners' experiences. In this paper, we focus on the Canadian context, sharing Indigenous learners' stories about their journey towards and throughout medical training. METHODS: The conceptual underpinnings of narrative inquiry and key principles from Indigenous methodologies were drawn upon throughout both data collection and analysis. Participants were Indigenous learners (medical students and residents) and a recently graduated physician (n = 5) from one Canadian medical school. Both spoken (formal recorded interviews) and visual (photographs) texts were used to make meaning of participants' experiences. RESULTS: Participants' experiences during medical training showed a striking resemblance at three points in their transition to, and progression through, medical education: preparing for and applying to medical school, completing undergraduate medical training and determining specialty choice. Participants' stories revealed a tug-of-war between their identities as an Indigenous person and as a medical trainee, with these tensions sometimes compromising their perceived sense of belonging within both Indigenous and academic circles, creating, at times, a heavy burden to shoulder. CONCLUSION: Meaningful representation of Indigenous people in the medical workforce is about more than training additional health care providers; it requires understanding Indigenous learners and recently graduated physicians' experiences as they enter and navigate the medical profession. By amplifying their voices, we stand to gain a more holistic representation of the factors that contribute to and potentially impede the recruitment and retention of Indigenous people into the medical profession.

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)0.005
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.033
GPT teacher head0.482
Teacher spread0.449 · 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.

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

Citations11
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueMedical EducationSame topicGlobal Health Workforce IssuesFrench-language works237,207