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Record W2909469617 · doi:10.12927/hcpol.2018.25690

Intersecting Policy Contexts of Employment-Related Geographical Mobility of Healthcare Workers: The Case of Nova Scotia, Canada

2018· article· en· W2909469617 on OpenAlexafffundvenueabout
Shiva Nourpanah, Ivy Lynn Bourgeault, Lois Jackson, Sheri Price, Pauline Gardiner Barber, Michael P. Leiter

Bibliographic record

VenueHealthcare policy · 2018
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of OttawaDalhousie University
FundersCanadian Institutes of Health ResearchInstitute of Gender and HealthResearch and Development Corporation of Newfoundland and Labrador
KeywordsNova scotiaResidenceWork (physics)Health careDemographic economicsNova (rocket)GeographyRegional scienceEconomic growthEconomicsEngineering

Abstract

fetched live from OpenAlex

Mobility and movement is an increasingly important part of work for many, however, Employment-Related Geographical Mobility (ERGM), defined as the extended movement of workers between places of permanent residence and employment, is relatively understudied among healthcare workers. It is critical to understand the policies that affect ERGM, and how they impact mobile healthcare workers. We outline four key intersecting policy contexts related to the ERGM of healthcare workers, focusing on the mobility of Registered Nurses (RNs), Licensed Practical Nurses (LPNs) and Continuing Care Assistants (CCAs) in Nova Scotia: international labour mobility and migration; interprovincial labour mobility; provincial credential recognition; and, workplace and occupational health and safety.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.960

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0180.005
Scholarly communication0.0060.001
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.046
GPT teacher head0.446
Teacher spread0.401 · 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 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

Citations5
Published2018
Admission routes4
Has abstractyes

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