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Facilitators and barriers to adjustment of international nurses: an integrative review

2009· review· en· W2052441576 on OpenAlexaboutno aff
Jennifer Kawi, Yan Xu

Bibliographic record

VenueInternational Nursing Review · 2009
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsNursingStaffingPsychosocialPsychologyHealth careAssertivenessScarcityRelevance (law)MedicinePolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: There is a scarcity of research focusing on issues encountered by international nurses (INs) in their adjustment to foreign health-care environments. Increasingly, INs are relied upon to address staffing shortages in many Western countries. As such, it is vital to identify what facilitates and what the barriers are to the successful adjustment in order to assist their integration into new workplace environments. AIM: This integrative review identifies facilitators and barriers encountered by INs as they adjust to foreign health-care environments. METHOD: Based on Cooper's Five Stages of Integrative Research Review, a systematic search of eight electronic databases was conducted, combined with hand and ancestral searches. Two authors independently reviewed each qualified study for relevance and significance. Subsequently, facilitators and barriers were identified and categorized into themes and subthemes. FINDINGS: Twenty-nine studies conducted in Australia, Canada, Iceland, UK and the USA were included in this review. Findings indicated that positive work ethic, persistence, psychosocial and logistical support, learning to be assertive and continuous learning facilitated the adjustment of INs to their new workplace environments. In contrast, language and communication difficulties, differences in culture-based lifeways, lack of support, inadequate orientation, differences in nursing practice and inequality were barriers. CONCLUSION: The review findings provide the basis for the development and testing of an evidence-informed programme to facilitate the successful adjustment of INs to their new work environments.

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)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.062
GPT teacher head0.536
Teacher spread0.474 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations114
Published2009
Admission routes1
Has abstractyes

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