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Record W2074541007 · doi:10.1080/14767724.2012.646901

Training ‘expendable’ workers: temporary foreign workers in nursing

2012· article· en· W2074541007 on OpenAlexaffabout
Alison Taylor, Jason Foster, Maria-Carolina Cambre

Bibliographic record

VenueGlobalisation Societies and Education · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsAthabasca UniversityUniversity of Alberta
Fundersnot available
KeywordsRestructuringClosure (psychology)RecessionHealth carePoliticsVocational educationTraining (meteorology)Economic shortageWork (physics)BusinessNursingPolitical scienceEconomic growthLabour economicsPublic relationsMedicineEconomicsGovernment (linguistics)Law

Abstract

fetched live from OpenAlex

The purpose of this article is to explore the experiences of Temporary Foreign Workers in health care in Alberta, Canada. In 2007–2008, one of the regional health authorities in the province responded to a shortage of workers by recruiting 510 health-care workers internationally; most were trained as Registered Nurses (RNs) in the Philippines. However, the Association of RNs required them to complete an assessment, and in many cases, to complete further training leading to an examination before they could actually work as RNs in the province. Furthermore, economic recession and restructuring of the health authority meant that many of the short-term contracts were not renewed, despite initial promises made by recruiters. This article looks at the assessment of foreign credentials and processes that followed as a part of the vocational education and training system that is often ignored. Drawing on social closure theories, we look at the experiences of foreign workers whose positions are extremely precarious in terms of employment and residency status. Our analysis suggests that the use of temporary workers to address ‘short term’ labour demand has implications for the workers themselves as well as larger political, social and economic implications that need to be acknowledged.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.707

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.090
GPT teacher head0.450
Teacher spread0.360 · 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

Citations11
Published2012
Admission routes2
Has abstractyes

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