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Overseas recruitment: experiences of nurses immigrating to Newfoundland and Labrador, 1949-2004

2010· article· en· W1960920488 on OpenAlexafffundabout
Marilyn Beaton, J. N. Walsh

Bibliographic record

VenueNursing Inquiry · 2010
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMemorial University of Newfoundland
FundersMemorial University of NewfoundlandAssociated Medical Services
KeywordsImmigrationEconomic shortageNursingGovernment (linguistics)Health careMedicinePolitical scienceGeographyArchaeology

Abstract

fetched live from OpenAlex

Overseas recruitment of nurses has been part of health-care in Newfoundland and Labrador, Canada for over a century. The International Grenfell Association began recruiting overseas in 1893 for nursing stations in Labrador and from the 1920s to 1940s, overseas recruitment was used to provide nurses for rural areas of the province. Beginning in the 1950s, government and provincial hospitals used this strategy to resolve nursing shortages as did Memorial University to attract faculty for the new nursing degree programme that started in the 1960s. Today, overseas recruitment continues for nurse midwives. Overseas recruitment brought challenges for those who came, for local nurses and for the profession. Many nurses returned home but others opted to stay. Overseas recruitment and the contribution these nurses made to nursing and health-care in Newfoundland and Labrador are significant but undocumented parts of our history. We undertook an oral history project to document their experiences, explore the challenges of overseas recruitment and preserve this record of nursing history. Forty-one nurses who immigrated to Newfoundland and Labrador between 1949 and 2004 and practised in all regions and settings were interviewed. Analysis of the data identified themes related to the nurses' immigration experience and adaptation to the culture and health-care of Newfoundland and Labrador.

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)0.000
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.097
GPT teacher head0.471
Teacher spread0.374 · 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 designQualitative
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

Citations19
Published2010
Admission routes3
Has abstractyes

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