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Record W1977496498 · doi:10.1177/01939450022044241

Cross-Cultural Hospital Care as Experienced by Mi’kmaq Clients

2000· article· en· W1977496498 on OpenAlexaffabout
Cynthia Baker, Monique Cormier Daigle, Anne Biro, Jennie R. Joe

Bibliographic record

VenueWestern Journal of Nursing Research · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsUniversity of AlbertaUniversité de Moncton
Fundersnot available
KeywordsCompassionContext (archaeology)NarrativeSociologyQualitative researchEmpathyPsychologyNursingSocial psychologyMedicineSocial sciencePolitical science

Abstract

fetched live from OpenAlex

The hospitalization experiences of members of a Mi’kmaq, First Nation Community in New Brunswick, Canada, were examined to understand cultural sensitivity from their perspectives. Interpretive interactionism, a qualitative methodology, guided the study. In-depth interviews were conducted with 10 participants from the Big Cove First Nation Community. Recurring themes in their narratives revolved around understanding. Participants often felt misunderstood by caregivers in a way that made them feel lessened as persons. They also had difficulty understanding the hospital milieu and felt that they were strangers in it. Each, however, had experienced valued encounters with at least one caregiver when they felt understood. They felt these caregivers had been kind to them, accepted the Mi’kmaq custom of family visiting, and treated them as social equals. Compassion and a nondiscriminatory attitude were more important than cultural knowledge in this context in which the caregivers’culture rather than the care recipients’ culture predominated.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.863
Threshold uncertainty score0.272

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.004
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.096
GPT teacher head0.532
Teacher spread0.436 · 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

Citations28
Published2000
Admission routes2
Has abstractyes

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