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Record W2533168215 · doi:10.1177/0844562116663951

Migrant Nurses and Federal Caregiver Programs in Canada

2016· article· en· W2533168215 on OpenAlexafffundvenueabout
Bukola Salami

Bibliographic record

VenueCanadian Journal of Nursing Research · 2016
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Alberta
KeywordsWorkforceImmigrationImmigration policyHealth human resourcesHuman resourcesHealth careHuman servicesNursingHealth policyEconomic growthPolitical sciencePublic relationsBusinessMedicineEconomics

Abstract

fetched live from OpenAlex

Despite the links between health human resources policy, immigration policy, and education policy, silos persist in the policy-making process that complicate the professional integration of internationally educated nurses in Canada. Drawing on the literature on nurse migration to Canada through the Live-in Caregiver Program, this paper sheds light on the contradictions between immigration and health human resources policy and their effect on the integration of internationally educated nurses in Canada. The analysis reveals a series of paradoxes within and across immigration and health human resources policy that affect the process of professional integration of this group of health professionals into the nursing workforce in Canada. I will further link the discussion to the recently implemented Caregiver Program, which provides a unique pathway for healthcare workers, including nurses, to migrate to Canada. Given recent introduction of the Canadian Caregiver Program, major policy implications include the need to bridge the gap between health human resources policy and immigration policy to ensure the maximum integration of migrant nurses in Canada.

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)0.001
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 score0.243
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.196
GPT teacher head0.489
Teacher spread0.293 · 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

Citations7
Published2016
Admission routes4
Has abstractyes

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