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Record W3122217801 · doi:10.12927/hcpol.2015.24211

Estimating Nursing Wage Bill in Canada and Breaking Down the Growth Rate: 2000 to 2010

2015· article· en· W3122217801 on OpenAlexaffvenueabout
Ruolz Ariste, Ali Béjaoui

Bibliographic record

VenueHealthcare policy · 2015
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsCenter for Interuniversity Research and Analysis on OrganizationsUniversité LavalUniversité du Québec en Outaouais
Fundersnot available
KeywordsWageWage growthWage rateLabour economicsEconomicsNursingDemographic economicsMedicine

Abstract

fetched live from OpenAlex

Even though the nursing professional category (registered nurses [RNs] and licensed practical nurses) made up about one-third of the Canadian health professionals, no study exists about their wage bill, the composition and growth rate of this wage bill. This paper attempts to fill this gap by estimating the nursing wage bill in the Canadian provinces and breaking down the growth rate for the 2000–2010 period, using the 2001 Census and the 2011 National Household Survey. Total wage bill for the nursing professional category in Canada was estimated at $20.1 billion ($17.3 billion for RNs), which suggests that it is as substantial as net physician remuneration. The average annual growth rate of this wage bill was 6.6% for RNs. This increase was mainly driven by real (inflation-adjusted) wage per hour, which was 3.0%, suggesting the existence of a “health premium” of 1.7 percentage points during the study period.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.068
GPT teacher head0.443
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 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

Citations2
Published2015
Admission routes3
Has abstractyes

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