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Record W2885486394 · doi:10.1177/1049732318792500

Interactions Between Indigenous Women Awaiting Childbirth Away From Home and Their Southern, Non-Indigenous Health Care Providers

2018· article· en· W2885486394 on OpenAlexafffundabout
Zoua M. Vang, Robert Gagnon, Tanya Lee, Vania Jimenez, Arian Navickas, Jeannie Pelletier, Hannah Shenker

Bibliographic record

VenueQualitative Health Research · 2018
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsCree Board of Health and Social Services of James BayMcGill University Health CentreMcGill University
FundersHealth Canada
KeywordsChildbirthIndigenousBureaucracyNursingHealth careMedicineFamily medicinePregnancyPolitical science

Abstract

fetched live from OpenAlex

We examine patient-provider interactions for Indigenous childbirth evacuees. Our analysis draws on in-depth interviews with 25 Inuit and First Nations women with medically high-risk pregnancies who were transferred or medevacked from northern Quebec to receive maternity care at a tertiary hospital in a southern city in the province. We supplemented the patient data with interviews from eight health care providers. Three themes related to patient-provider interactions are discussed: evacuation-related stress, hospital bureaucracy, and stereotypes. Findings show that the quality of the patient-provider interaction is contingent on individual health care providers' ability to connect with Indigenous patients and overcome cultural and institutional barriers to communication and trust-building. The findings point to the need for further training of medical professionals in the delivery of culturally safe care and addressing bureaucratic constraints in the health care system to improve patient-provider communication and overall relationship quality.

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)0.004
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 score0.113
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.195
GPT teacher head0.530
Teacher spread0.335 · 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

Citations28
Published2018
Admission routes3
Has abstractyes

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