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Record W2100119464 · doi:10.1177/1049732314566325

Visibility and Voice

2015· article· en· W2100119464 on OpenAlexafffundabout
Rachelle Hole, Mike Evans, Lawrence D. Berg, Joan L. Bottorff, Carlene Dingwall, Carmella Alexis, Jessie C. Nyberg, Michelle L. Smith

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsParticipatory action researchIndigenousVisibilityFeelingInterpersonal communicationCultural safetyNarrativeCitizen journalismNursingHealth careAction (physics)PsychologyMedicineQualitative researchPublic relationsSociologySocial psychologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

In Canada, cultural safety (CS) is emerging as a theoretical and practice lens to orient health care services to meet the needs of Aboriginal people. Evidence suggests Aboriginal peoples' encounters with health care are commonly negative, and there is concern that these experiences can contribute to further adverse health outcomes. In this article, we report findings based on participatory action research drawing on Indigenous methods. Our project goal was to interrogate practices within one hospital to see whether and how CS for Aboriginal patients could be improved. Interviews with Aboriginal patients who had accessed hospital services were conducted, and responses were collated into narrative summaries. Using interlocking analysis, findings revealed a number of processes operating to produce adverse health outcomes. One significant outcome is the production of structural violence that reproduces experiences of institutional trauma. Positive culturally safe experiences, although less frequently reported, were described as interpersonal interactions with feelings visibility and therefore, treatment as a "human being."

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)0.059
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0590.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0080.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.567
GPT teacher head0.654
Teacher spread0.087 · 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

Citations90
Published2015
Admission routes3
Has abstractyes

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