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Record W1523060541

Influence of Education Portability and Geographical Mobility on Nursing Retention in Canada

2011· article· en· W1523060541 on OpenAlexaboutno aff
Zelalem Oldjira Lome

Bibliographic record

VenueJournal of Global Health Care Systems · 2011
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsSoftware portabilitySalaryWorkforceEconomic shortageNursing researchNursingBusinessNursing shortageNurse educationMedicinePolitical scienceEconomic growthEconomicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

This review analysis evaluates the influence of agreement on internal trade (AIT) on retention of the registered nurses (RNs) in Canada. The 1994 AIT is an agreement between the federal, provincial, and Territories of Canadian governments to create an inclusive labor market for the employed, unemployed, under employed, through removal of barriers, and to enhance employment opportunities across Canada. This quantitative correlational review analysis is designed to evaluate the correlations between diploma RNs education portability, geographic mobility, the various levels of salary scales between the provinces, and its influence on RN retention after the AIT implementation in Canada. Data from the Canadian Institute for Health Information (CIHI) database is used for the review analysis. The review analysis provides the policy history, research question, and hypothesis. Furthermore, literature review and a review research design are presented consecutively. Finally, recommendations are given for AIT revision, and on strategies for increasing RN retention in Canada. Keywords: nursing retention, nursing shortage, nursing workforce, nursing mobility, agreement on internal trade, quantitative, RN education portability, geographical mobility

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.409
Teacher spread0.381 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2011
Admission routes1
Has abstractyes

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