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Record W4245787636 · doi:10.3138/cpp.38.2.167

Impact of Public Policy on Nursing Employment: Providing the Evidence

2012· article· en· W4245787636 on OpenAlexaffvenueabout
Andrea Baumann, Mabel Hunsberger, Mary Crea‐Arsenio

Bibliographic record

VenueCanadian Public Policy · 2012
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCasualWorkforcePublic policyStimulus (psychology)Government (linguistics)Labour economicsNursingBusinessDemographic economicsPolitical scienceEconomicsPsychologyEconomic growthMedicine

Abstract

fetched live from OpenAlex

Due to economic instability, employment status has been shifting over time. Organizations have been moving toward a flexible contingent workforce. An early study of employment patterns demonstrated a significant rise in part-time and casual employment in the Ontario nursing workforce ( Baumann et al. 2006 ). The Nursing Graduate Guarantee, a public policy initiative, represents a substantial investment by the provincial government to stimulate full-time employment. This article presents the results of a trend analysis of nurse employment. Results indicated that stimulus funding attached to the public policy influenced employment of new graduate nurses.

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)0.082
metaresearch head score (Gemma)0.271
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.371
Threshold uncertainty score0.746

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0820.271
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0090.016
Science and technology studies0.0040.009
Scholarly communication0.0140.007
Open science0.0040.005
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0170.001

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 head0.194
GPT teacher head0.510
Teacher spread0.317 · 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 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

Citations9
Published2012
Admission routes3
Has abstractyes

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