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Record W6886088313 · doi:10.14288/1.0363451

History, geography and ethics of health worker migration in Canada

2018· other· en· W6886088313 on OpenAlexaboutno aff

Bibliographic record

VenuecIRcle (University of British Columbia) · 2018
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMainlandImmigrationPresentation (obstetrics)Public healthMainland ChinaHealth care

Abstract

fetched live from OpenAlex

Presented by the Irving K. Barber Learning Centre and Woodward Library, the Health Information Series is an ongoing public lecture series that takes place in the Lower Mainland community. Hosted by the Burnaby Public Library’s Tommy Douglas Branch Library, Dr. Geertje Boschma will be giving a presentation that explores issues of health worker migration through examining the history, geography, and ethics of international recruitment and migration of health workers to Canada, and focusing on the experiences of registered nurses from the Philippines who have migrated to Canada. During the past few decades the migration of Filipino nurses to Canada has considerably expanded, with nurses from the Philippines making up the largest group of all immigrant nurses in the Canadian workforce. What are the implications of these trends for the healthcare community?

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.163
Threshold uncertainty score0.971

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0430.036
Scholarly communication0.0100.002
Open science0.0020.006
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.184
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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