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Record W4377010355 · doi:10.1515/ijnes-2022-0106

Faculty experiences of teaching internationally educated nurses: a qualitative study

2023· article· en· W4377010355 on OpenAlexaffabout
Gail N. Crockford, Barbara Pesut, Katrina Plamondon, Randy Janzen

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsSelkirk CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaMount Royal University
Fundersnot available
KeywordsQualitative researchMedical educationPublic healthMedicineNursingPsychologyPedagogySociologySocial science

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of this study was to explore the experiences of faculty teaching in programs designed to support internationally educated nurses' transition to nursing practice in Canada. METHODS: This was a qualitative study that gathered data through semi-structured interviews. RESULTS: Four themes were developed from the data: learning the learner, feeling moral unrest in my role, inviting reciprocal relationships, and finding our way. CONCLUSIONS: There is an urgent need to ensure that faculty are well prepared for their role and that the needs of internationally educated nurses, both personal and pedagogical, are central. Despite the challenges experienced by faculty, they also describe great growth as a result of their new role. IMPLICATIONS FOR AN INTERNATIONAL AUDIENCE: Findings from this study are particularly relevant for those in high income countries seeking to support internationally educated nurses. Faculty preparedness and holistic support for students are critical for ethical, high-quality education.

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)0.013
metaresearch head score (Gemma)0.020
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 score0.025
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.009
Scholarly communication0.0050.004
Open science0.0020.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.239
GPT teacher head0.645
Teacher spread0.406 · 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
Published2023
Admission routes2
Has abstractyes

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