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

2024· article· en· W4392550046 on OpenAlexaffabout
Kateryna Metersky, Kaveenaa Chandrasekaran, Suzanne Ezekiel

Bibliographic record

VenueNursing Education Perspectives · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousThematic analysisCurriculumNursingNurse educationHealth careContent analysisMedical educationPsychologyMedicineQualitative researchSociologyPedagogyPolitical scienceSocial science

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 score0.480
Threshold uncertainty score0.965

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0160.007
Scholarly communication0.0050.001
Open science0.0020.007
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.410
Teacher spread0.388 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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