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Record W2947762672 · doi:10.1111/inr.12518

The evaluation of evidence‐informed changes to an internationally educated nurse registration process

2019· article· en· W2947762672 on OpenAlexafffundabout
Jennifer Kwan, M. Wang, Greta G. Cummings, Gillian Lemermeyer, Pamela M. Nordstrom, Lawrence S. Blumer, Neil Horne, Cathy Giblin

Bibliographic record

VenueInternational Nursing Review · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsCollege & Association of Registered Nurses of AlbertaMount Royal UniversityUniversity of Alberta
FundersAlberta HealthMount Royal University
KeywordsTimelineNursingProcess (computing)GlobeNursing shortageTransparency (behavior)Health policyPsychologyMedicineMedical educationNurse educationPolitical scienceComputer sciencePublic health

Abstract

fetched live from OpenAlex

AIM: To evaluate effectiveness of specific policy and practice changes to the process of registration for internationally educated nurses. BACKGROUND: Little research exists to inform registration policy for internationally educated health professionals. INTRODUCTION: Internationally educated nurse employment can help address nursing shortages. Regulators assess competencies for equivalency to Canadian-educated nurses, but differences in health systems, education and practice create challenges. METHODS: The study setting was a Canadian province. We used a mixed methods approach, with a pre-post-quasi-experimental design and a qualitative evaluation. Previous analysis of relationships between applicant variables, registration outcomes and timelines informed changes to our registration process. Implementation of these changes composes the intervention. Comparisons between pre- and post-implementation exemplar subgroups and timeline analyses were conducted using descriptive statistics, univariate analysis and non-parametric tests. Data were collected from complete application files before (n = 426) and after (n = 287) implementation of the intervention. Interviews, focus groups and consultations were completed with various stakeholders. FINDINGS: The time between steps in the process was significantly reduced following implementation. Stakeholders reported an increase in perceived efficiency, transparency and use of evidence. DISCUSSION: Results indicated that initial impacts of the policy changes streamlined the process for applicants and staff. CONCLUSION: Maintaining a consistent and systematic review of an organization's data coupled with implementation of findings to effect policy and practice change may have an important impact on regulatory policy. IMPLICATIONS FOR NURSING POLICY: These findings represent the beginning of an international policy conversation. Policy changes based on organizational data can underlie major process improvement initiatives. Ongoing nursing shortages across the globe and increasing mobility of nurses make it important to have efficient and transparent regulatory policy informed by evidence.

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 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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.298
GPT teacher head0.621
Teacher spread0.323 · 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 teacher head, not a consensus.

Study designOther design
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

Citations12
Published2019
Admission routes3
Has abstractyes

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