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Record W1991819643 · doi:10.1097/hcm.0000000000000018

Foreign-Trained Nurses’ Experiences and Socioprofessional Integration Best Practices

2014· review· en· W1991819643 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Health Care Manager · 2014
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsWorkforceEconomic shortageContext (archaeology)Best practiceProcess (computing)PsychologyNursingPublic relationsBusinessSociologyPolitical scienceMedicineComputer scienceHistoryLaw

Abstract

fetched live from OpenAlex

This article examines the evidence available on obstacles and facilitating factors for the socioprofessional integration of internationally educated nurses (IENs) and tries to generate best practices concerning their workforce integration. In the nursing shortage context, more and more attention is given to IEN recruitment. Still, IENs' integration experiences into their new environment are strenuous. Differences in nursing practice and in cultural values, communicational barriers, discrimination, and competency recognition delays complicate this transition. Yet few guidelines are found concerning the best practices to implement to ease this process. This literature review suggests the necessity for a collaborative approach of IEN integration.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.141
GPT teacher head0.551
Teacher spread0.411 · 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