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Record W2974476472 · doi:10.1080/14461242.2019.1659154

Healthcare workers ‘on the move’: making visible the employment-related geographic mobility of healthcare workers

2019· article· en· W2974476472 on OpenAlexaffabout
Lois Jackson, Sheri Price, Pauline Gardiner Barber, Audrey Kruisselbrink, Michael P. Leiter, Shiva Nourpanah, Ivy Lynn Bourgeault

Bibliographic record

VenueHealth Sociology Review · 2019
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversity of OttawaAcadia UniversityDalhousie University
Fundersnot available
KeywordsHealth careProject commissioningHealthcare workerPublishingPublic relationsSociologyBusinessEconomic growthPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Many healthcare workers are ‘on the move’ as part of their employment, travelling often great distances to such places as patients’/clients’ homes and community clinics. Healthcare workers’ experiences of this employment-related geographic mobility have been relatively invisible even though mobility is necessary for home and community care. Interviews with professional (e.g. nurses) and paraprofessional (e.g. personal care assistants) healthcare workers in Nova Scotia (Canada) found that mobility includes safety risks, and health and economic costs, although a few professionals had employment contracts that helped to protect them against such risks and costs. Paraprofessionals appear to be most impacted by the economic costs given their lower incomes. Many healthcare workers also experienced travel positively, as time away from fixed sites, and associated this time with freedom. The risks of mobility were understood by some workers as part of a duty to care, but a few suggested that the health and economic costs are an undue burden, pointing to an opening for challenging these conditions. There is a need for regulations to ensure all healthcare workers are safe as they are mobile to and from fixed sites, and do not have to shoulder the health or economic costs of mobility.

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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.594
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.087
GPT teacher head0.448
Teacher spread0.361 · 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.

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
Published2019
Admission routes2
Has abstractyes

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