MétaCan
Menu
Back to cohort
Record W2620911947 · doi:10.1111/nin.12204

Applying cultural safety beyond Indigenous contexts: Insights from health research with Amish and Low German Mennonites

2017· article· en· W2620911947 on OpenAlexafffund
Amélie Blanchet Garneau, Helen M. Farrar, HaiYan Fan, Judith C. Kulig

Bibliographic record

VenueNursing Inquiry · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsUniversity of LethbridgeUniversité de MontréalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsGermanSociocultural evolutionSociologyIndigenousContext (archaeology)Perspective (graphical)Face (sociological concept)Power (physics)Grounded theoryPublic relationsGender studiesQualitative researchSocial sciencePolitical scienceAnthropologyGeography

Abstract

fetched live from OpenAlex

People who identify as members of religious communities, such as the Amish and Low German Mennonites, face challenges obtaining quality health care and engagement in research due in part to stereotypes that are conveyed through media and popular discourses. There is also a growing concern that even when these groups are engaged in research, the guiding frameworks of the research fail to consider the sociocultural or historical relations of power, further skewing power imbalances inherent in the research relationship. This paper aims at discussing the uses of cultural safety in the context of health research and knowledge translation with groups of people that are associated with a specific religion. Research with the Amish and Low German Mennonites is provided as examples to illustrate the use of cultural safety in this context. From these examples, we discuss how the use of cultural safety, grounded in critical theoretical perspectives, offers new insight into health research with populations that are traditionally labeled as minority, vulnerable, or marginalized, especially when a dominant characteristic is a unique religious perspective.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
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 head0.080
GPT teacher head0.351
Teacher spread0.270 · 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 designObservational
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

Citations23
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueNursing InquirySame topicAgriculture and Farm SafetyFrench-language works237,207