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Record W4411615800 · doi:10.32799/ijih.v20i2.43274

Improving Culturally Appropriate Care for Indigenous Women and Their Families During the Birthing/Delivery Experience

2025· article· en· W4411615800 on OpenAlexaffvenueabout
Natalie Vanidour, Holly Graham

Bibliographic record

VenueInternational Journal of Indigenous Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsIndigenousCulturally appropriateNursingCulturally sensitivePsychologyMedicineSociologyGerontologySocial psychologyEcologyBiology

Abstract

fetched live from OpenAlex

Current literature highlights culturally appropriate care as a useful tool in reducing negative health outcomes caused by systemic racism in healthcare. This research adds to the literature of ways to enhance culturally appropriate care to promote positive birthing/delivery experiences for Indigenous (First Nation, Métis, and Inuit) women and their families. This study is strength-based and aims to provide meaningful suggestions to augment culturally safe care as a response to the Truth and Reconciliation Commission of Canada’s Calls to Action. Six participants responded to an online survey and shared what positive actions could enhance a culturally safe birth. The survey data was analyzed using inductive thematic analysis and descriptive statistical analysis. The results are discussed under three themes: 1) Access to Cultural Supports; 2) Relationships with Healthcare Providers; and 3) Examples of Harmful Nursing Practice. Based on the participant responses, it is suggested that respect, active allyship, enhanced cultural safety education, and integration of Indigenous healing practices into the Canadian healthcare system will support the delivery of culturally appropriate care. The data supports cultural safety as an important aspect of the birthing experience.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.008
GPT teacher head0.297
Teacher spread0.289 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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