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Record W2623507600

The lived experience of internationally educated nurses with developing intercultural competence in Ontario after watching communication vignettes

2013· dissertation· en· W2623507600 on OpenAlexaboutno aff
Jessica Catherine Coulis

Bibliographic record

VenueYork University Digital Library (York University) · 2013
Typedissertation
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsIntercultural competenceCompetence (human resources)Intercultural communicationLived experienceCultural competencePsychologyPedagogySocial psychologyPsychotherapist
DOInot available

Abstract

fetched live from OpenAlex

Internationally educated nurses (IEN s) are nurses who obtain their nursing license outside of Canada. IENs face many challenges when trying to transition into the Canadian healthcare system. This is largely due to their misunderstanding intercultural competence (ICC). The purpose of this study was to understand the meaning of the IENs' lived experiences with developing ICC. Forty-six participants took part in focus group discussions based on culturally specific vignettes developed in congruence with the College of Nurses of Ontario Standards of Practice. Using hermeneutic phenomenology and secondary analysis three themes emerged from the data: navigating the headwater, propelling in a new direction and mapping the way. The above themes led to the emergence of the essential theme- confidence. The IENs rely on the confidence engrained in their 'old' knowledge in order to understand and develop their ICC. This study addresses the gap in research relating to IENs transition into Canadian nursing.

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)0.003
metaresearch head score (Gemma)0.010
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 score0.238
Threshold uncertainty score0.479

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0190.013
Scholarly communication0.0050.002
Open science0.0020.008
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.277
Teacher spread0.253 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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