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Record W4408777705 · doi:10.32920/28646606

Undergraduate Nursing Student Reflections on Indigenous Peoples’ Experiences With the Canadian Health Care System

2025· preprint· en· W4408777705 on OpenAlexaboutno aff
Kateryna Metersky, Kaveenaa Chandrasekaran, Suzanne Ezekiel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousNursingHealth careMedicinePolitical scienceSociology

Abstract

fetched live from OpenAlex

AIM The aim of this study was to analyze nursing student level of knowledge and understanding of current experiences of Indigenous people within the Canadian health care system to identify curricular gaps that need to be addressed. BACKGROUND In response to the 2015 Truth and Reconciliation Commission of Canada, nursing schools have begun incorporating Indigenous health content into curricula. However, few studies about the implementation and effectiveness of this education exist. METHOD Students wrote a reflection and engaged with colleagues’ reflections after watching a video from the Aboriginal Peoples Television Network. Fifteen reflections were selected using systematic, random sampling to undergo thematic analysis. RESULTS Two themes were identified: 1) students’ understanding of barriers Indigenous populations face when accessing the health care system and 2) students’ perceptions of strategies to ensure culturally safe care for Indigenous populations. CONCLUSION Analysis of students’ learning through a reflection activity can improve the Indigenous health content curriculum.

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)0.007
metaresearch head score (Gemma)0.009
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 score0.216
Threshold uncertainty score0.436

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0270.010
Scholarly communication0.0070.002
Open science0.0030.010
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.056
GPT teacher head0.433
Teacher spread0.377 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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