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Record W2163310564 · doi:10.1177/1043659609348623

Transnational Cultural Ecologies: Evolving Challenges for Nurses in Canada

2009· article· en· W2163310564 on OpenAlexaffabout
S Isaacs

Bibliographic record

VenueJournal of Transcultural Nursing · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Competency in Health Care
Canadian institutionsMcMaster UniversityPublic Health Agency of Canada
Fundersnot available
KeywordsAcculturationHealth careImmigrationPublic relationsPreferenceSociologyPolitical scienceNursingMedicine

Abstract

fetched live from OpenAlex

Canada is experiencing an evolving cultural ecology as new arrivals of immigrants now realize opportunities for sustaining familiar ties with home countries through advancing technologies and travel. Those arriving will have diverse experiences and preferences, many with opportunities for meeting their health needs elsewhere. For those less privileged, options for health care and health enabling resources are more limited as existing health systems continue to give preference to a dominant culture based on a European heritage-even though, progressively, Canadian society becomes more diverse in its cultural makeup. We as nurses and others engaged in health care systems need to consider our own acculturation processes as we adapt to the changes happening in our society. Systemic approaches to cultural competency in health care need to be considered that enable nurses and other health care providers to be adaptive and resilient in a transnational world.

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)0.006
metaresearch head score (Gemma)0.008
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 score0.101
Threshold uncertainty score0.732

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0470.009
Scholarly communication0.0100.003
Open science0.0030.016
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.059
GPT teacher head0.372
Teacher spread0.313 · 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

Citations11
Published2009
Admission routes2
Has abstractyes

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